System and method for natural language processing

ABSTRACT

A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Continuation-In-Part patent application claims the benefit andpriority to U.S. Continuation patent application Ser. No. 17/123,665filed Dec. 16, 2020, which claims priority to U.S. patent applicationSer. No. 17/123,559 filed Dec. 16, 2020, the entire disclosures of whichare hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates to data object management, and in particular tosystems and methods for processing natural language textual informationof corresponding data objects.

BACKGROUND

Medications, such as prescription medications, over-the-countermedications, vitamins, supplements, and the like, are increasingly beingdelivered by a medication provider, such as a large volume pharmacy andthe like, to a residence or other location of an individual requiringsuch medications. Medications may be delivered using a variety ofdelivery services, such as a postal service, a parcel delivery service,a contractor, or other service under direct control of a correspondingmedication provider, and the like.

Management of such a medication provider, such as management of thedelivers, management of safety regulations corresponding to the storageand distribution of various pharmacological materials, management of aworkforce supporting the medication provider, management of othervarious systems of the medication provider, and the like, may requirevarious program and project management systems and methods. Typically,such program and project management systems and methods compete forresources during the same or substantially overlapping periods.Additionally, or alternatively, use of such management systems andmethods may result in tangible and/or intangible benefit to themedication provider. Such tangible and/or intangible benefits may varyfor individual ones of the management systems and methods.

SUMMARY

This disclosure relates generally to natural language processing of dataobjects.

An aspect of the disclosed embodiments includes a system for processingnatural language includes. The system includes a processor and a memory.The memory includes instructions that, when executed by the processor,cause the processor to: receive at least one data object; identify, fora first aspect of the at least one data object that includes free formtextual information, text strings of the free form textual informationhaving a first text string type; in response to identifying at least onetext string of the free form textual information having the first textstring type, generate updated free form textual information by removingthe at least one text string having the first text string type; generateone or more feature vectors based on the updated free form textualinformation using at least one of a unigram, a bigram, and a trigram;use an artificial intelligence engine that uses at least one machinelearning model configured to provide, using the one or more featurevectors, an output that includes at least one prediction indicating atleast one resource domain and a weight value indicating a probabilitythat the at least one resource domain corresponds to the free formtextual information; and provide, at a display, the output.

Another aspect of the disclosed embodiments includes a method forprocessing natural language. The method includes receiving at least onedata object and identifying, for a first aspect of the at least one dataobject that includes free form textual information, text strings of thefree form textual information having a first text string type. Themethod also includes, in response to identifying at least one textstring of the free form textual information having the first text stringtype, generating updated free form textual information by removing theat least one text string having the first text string type. The methodalso includes generating one or more feature vectors based on theupdated free form textual information using at least one of a unigram, abigram, and a trigram. The method also includes using an artificialintelligence engine that uses at least one machine learning modelconfigured to provide, using the one or more feature vectors, an outputthat includes at least one prediction indicating at least one resourcedomain and a weight value indicating a probability that the at least oneresource domain corresponds to the free form textual information. Themethod also includes providing, at a display, the output.

Another aspect of the disclosed embodiments includes an apparatus forprocessing natural language includes a processor and a memory. Thememory includes instructions that, when executed by the processor, causethe processor to: receive at least one data object having an aspect thatincludes free form textual information corresponding to a projectdescription; identify text strings of the free form textual informationhaving a first text string type; in response to identifying at least onetext string of the free form textual information having the first textstring type, generate updated free form textual information by removingthe at least one text string having the first text string type; generatea plurality of feature vectors using the updated free form textualinformation, wherein each feature vector of the plurality of featurevectors includes at least one of a unigram, a bigram, and a trigram; usean artificial intelligence engine that uses at least one machinelearning model configured to provide, using the plurality of featurevectors, an output that includes at least one prediction indicating atleast one resource domain and a weight value indicating a probabilitythat the at least one resource domain corresponds to the at least onedata object, wherein the machine learning model is initially trainedusing a supervised learning technique and iteratively trained using atleast the output of the at least one machine learning model; andprovide, at a display, the output.

These and other aspects of the present disclosure are disclosed in thefollowing detailed description of the embodiments, the appended claims,and the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1A generally illustrates a functional block diagram of a systemincluding a high-volume pharmacy according to the principles of thepresent disclosure.

FIG. 1B generally illustrates a computing device according to theprinciples of the present disclosure.

FIG. 2 generally illustrates a functional block diagram of a pharmacyfulfillment device, which may be deployed within the system of FIG. 1A.

FIG. 3 generally illustrates a functional block diagram of an orderprocessing device, which may be deployed within the system of FIG. 1A.

FIGS. 4A-4C generally illustrate data object combinations according tothe principles of the present disclosure.

FIG. 5 is a flow diagram generally illustrating a dynamic data objectscoring method according to the principles of the present disclosure.

FIGS. 6A and 6B is a flow diagram generally illustrating a data objectevolutionary optimization method according to the principles of thepresent disclosure.

FIG. 7 is a flow diagram generally illustrating a natural languageprocessing method according to the principles of the present disclosure.

FIG. 8 generally illustrates layers of a machine learning modelaccording to the principles of the present disclosure.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of theinvention. Although one or more of these embodiments may be preferred,the embodiments disclosed should not be interpreted, or otherwise used,as limiting the scope of the disclosure, including the claims. Inaddition, one skilled in the art will understand that the followingdescription has broad application, and the discussion of any embodimentis meant only to be exemplary of that embodiment, and not intended tointimate that the scope of the disclosure, including the claims, islimited to that embodiment.

As described, medications, such as prescription medications,over-the-counter medications, vitamins, supplements, and the like, areincreasingly being delivered by a medication provider, such as a largevolume pharmacy and the like, to a residence or other location of anindividual requiring such medications. Medications may be deliveredusing a variety of delivery services, such as a postal service, a parceldelivery service, a contractor, or other service under direct control ofa corresponding medication provider, and the like.

Management of such a medication provider, such as management of thedelivers, management of safety regulations corresponding to the storageand distribution of various pharmacological materials, management of aworkforce supporting the medication provider, management of othervarious systems of the medication provider, and the like, may requirevarious program and project management systems and methods

Such program and project management systems and methods compete forresources during the same or substantially overlapping periods. Forexample, various projects may be initiated to implement, upgrade, orotherwise utilize various management systems and methods. Such projectsmay utilize one or more domains (e.g., groups of workforce resourcesspecializing in one or more technological or other area of expertise) toimplement, upgrade, or otherwise utilize one or more of the variousmanagement systems and methods. Further, such projects may haveassociated capital or other resource costs, including, but not limitedto, domain usage costs. Additionally, or alternatively, use of suchmanagement systems and methods may result in tangible and/or intangiblebenefit to the medication provider. Such tangible and/or intangiblebenefits may vary for individual ones of the management systems andmethods.

Typically, various stakeholders within the medication providerorganization may review such project costs and benefits and decide whichproject or group of projects should move forward, given variousconstraints (e.g., budget constraints, time constraints, resourceconstraints, etc.). Such decisions tend to be made using manual reviewof various information corresponding to the various projects.Additionally, such manual review by the various stakeholders may bebiased and time consuming, and, in some cases, may lack criticalinformation and/or analysis, which may render the decision makingsuboptimal.

According, systems and methods, such as those described herein,configured to dynamically score such projects and/or identify groups ofprojects that can be undertaken given various constraints, may bedesirable. In some embodiments, the systems and methods described hereinmay be configured to provide dynamic common value scoring of the variousprojects. The systems and methods described herein may be configured toprovide data-driven value prioritization of the various projects. Thesystems and methods described herein may be configured to provideenterprise-ranked opportunity canvases (e.g., reports and/or other dataoutput). The systems and methods described herein may be configured toprovide cost calculations, capacity constraint planning, data analysis,and the like for the various projects. The systems and methods describedherein may be configured to provide preliminary impact analysis for thevarious projects. The systems and methods described herein may beconfigured to provide optimized project combination planning.

In some embodiments, the systems and methods described herein may beconfigured to simulate all variations of how the various projects can besequenced. It should be understood that, while limited examples ofmedication providers, such as a high volume pharmacy, are describedherein, the principles of the present disclosure are applicable to anysuitable application, such as military strategy planning, softwarerollout planning, vehicle model rollout planning, vaccine developmentplanning, vaccine manufacturing planning, vaccine distribution planning,and the like. Additionally, or alternatively, it should be understoodthat, while projects are described herein, the principles of the presentdisclosure apply to any suitable input. The systems and methodsdescribed herein may be configured to return or provide a most valuableor a substantially most valuable combination of projects for a givenproject timeframe. The systems and methods described herein may beconfigured to generate recommendations used by members of one or moredomains to provide cross-functional domain output.

In some embodiments, the systems and methods described herein may beconfigured to receive project information input. The project informationinput may include one or more data objects. A data object may includeinformation corresponding to a project questionnaire. The projectquestionnaire may include a plurality of data input fields. The datainput fields may be provided to a user via a user interface. The userinterface may be provided to the user via a suitable computing device,such as those described herein.

In some embodiments, the project questionnaire data input fields may beconfigured to allow the user to provide information corresponding to arespective project. In some embodiments, the data input fields mayinclude drop down selection fields, radio button selection fields, freeform text fields, other suitable data input fields, or a combinationthereof. In some embodiments, the user may interact with the userinterface to provide information (e.g., answers) corresponding torequests for information (e.g., questions) on the project questionnaire.

In some embodiments, the requests for information may include aplurality of request for information types. For example, the request forinformation types may include a time criticality request for informationtype, a business value request for information type, a risk reductionand opportunity enablement request for information type, an effortrequest for information type (e.g., indicating an effort associated withthe respective project), any other suitable request for informationtype, or a combination thereof. A request for information type may beassociated with one or more requests for information on the projectquestionnaire. For example, a first request for information type maycorresponding to a plurality of requests for information. The firstrequest for information type may correspond to the time criticalityrequest for information type or other suitable request for informationtype. The plurality of requests for information may include a pluralityof questions associated with time criticality of a corresponding project(e.g., “what does the time criticality curve look like?”, “if the needby date is missed, is there value lost?”, and the like).

In some embodiments, each of the requests for information of the projectquestionnaire includes an assigned weight value. Additionally, oralternatively, each of the requests for information having apre-validated input (e.g., an input or response to a request forinformation that the user can select) may include an assigned scorevalue. In some embodiments, the systems and methods described herein maybe configured to determine a sum of all weighted score values for arequest for information type. The systems and methods described hereinmay be configured to divide, for the respective request for informationtype, a corresponding weighted score value by a weight max value.

In some embodiments, the user may input information using the userinterface to respond to the requests for information of the projectquestionnaire. The project questionnaire may be organized in multiplesections corresponding to the request for information types. Forexample, the project questionnaire may be organized into four sectionseach representing a respective request for information type (e.g., orother suitable number of sections). In some embodiments, the systems andmethods described herein may be configured to determine a common valuescore for each respective project corresponding to a projectquestionnaire. For example, the systems and methods described herein maybe configured to determine a sum of a first request for information typescore (e.g., corresponding to a time criticality score), a secondrequest for information type score (e.g., corresponding to a businessvalue score), and a third request for information type score (e.g.,corresponding to a risk reduction and opportunity enablement score). Thesystems and methods described herein may be configured to determine thecommon value score for the respective project by determining the resultof the first sum by a fourth request for information type score (e.g.,corresponding to an effort score).

In some embodiments, the systems and methods described herein may beconfigured to store the common value score for each respective projectin a data store, such as a database (e.g., a standard query languagedatabase or other suitable data store). The systems and methodsdescribed herein may be configured to sort the common value scores forthe respective projects in according to a sorting algorithm. Forexample, the systems and methods described herein may be configured tosort the common value scores in a descending order or other suitableorder.

In some embodiments, the systems and methods described herein may beconfigured to generate one or more reports (e.g., which may be referredto as an opportunity canvas). For example, the systems and methodsdescribed herein may be configured to receive the common value scoresfrom the data store. The systems and methods described herein may beconfigured to generate an output indicating the common value scoresand/or any suitable information corresponding to each of the commonvalue scores (e.g., project name, domain utilization, etc.). The systemsand methods described herein may be configured to provide the output toa display, such as those described herein, and/or a file stored in asuitable memory.

In some embodiments, the systems and methods described herein may beconfigured to calibrate common value scoring based on feedback receivedfrom the user and/or other users. For example, the systems and methodsdescribed herein may be configured to revise various requests forinformation of the project questionnaire, revise various calculations,and the like based on the feedback.

In some embodiments, the systems and methods described herein may beconfigured to generate a data object, representing the informationprovided by the user in response to the requests for information on theproject questionnaire, for a respective project. The data object mayinclude any suitable data object and may follow any data object protocolor data structure. In some embodiments, the systems and methodsdescribed herein may be configured to identify information associatedwith one or more received data objects. For example, the systems andmethods described herein may be configured to identify informationcorresponding to one or more requests for information of the projectquestionnaire.

In some embodiments, the information corresponding to the one or morerequests for information represented in the one or more data objects mayinclude numerical information (e.g., a first type of pre-validatedinput), fixed textual information (e.g., a second type of pre-validatedinput including one or more predefined text strings recognizable by thesystems and methods described herein), free form textual information,other suitable information, or a combination thereof. The systems andmethods described herein may be configured to recognize various aspectsof the information corresponding to the one or more requests forinformation represented by the one or more data objects, such asnumerical information, fixed textual information, and the like.

In some embodiment, the systems and methods described herein may beconfigured to identify information corresponding to the one or morerequests for information that includes free form textual informationusing one or more natural language processing techniques. For example,the systems and methods described herein may be configured to processthe free form textual information and generate output that representsthe free form textual information. The systems and methods describedherein may use the generated output to, at least, dynamically score theone or more projects represented by the one or more data objects. Forexample, the systems and methods described herein may be configured toidentify one or more domains associated with a respective projectrepresented by a corresponding data object.

In some embodiments, the systems and methods described herein may beconfigured to use an artificial intelligence engine that uses at leastone machine learning model configured to perform natural languageprocessing techniques. The systems and methods described herein may beconfigured to use the artificial intelligence engine to identify theinformation corresponding to the one or more requests for informationthat include free form textual information.

In some embodiments, the systems and methods described herein may beconfigured to receive a first data object, as described. It should beunderstood that the systems and methods described herein may beconfigured to receive any suitable number of data objects. The firstdata object may represent information corresponding to requests forinformation associated with a project questionnaire for a respectiveproject. The first data object may indicate a first value associatedwith a first value type. The first value type may correspond to one ofthe requests for information types, as described. The first value maycorrespond to information for a respective data input field on theproject questionnaire. For example, the first value may correspond toinformation the user provided at the user interface in response to arequest for information associated with the first value type.

In some embodiments, the first value may include a first weight value.The first data object may further indicate a second value associatedwith the first value type. The second value may correspond toinformation for another respective data input field on the projectquestionnaire. For example, the second value may correspond toinformation the user provided at the user interface in response toanother request for information associated with the first value type(e.g., the project questionnaire may include multiple requests forinformation corresponding to a respective request for information type).The second value may include a second weight value.

In some embodiments, the first data object may further indicate a thirdvalue and a fourth value associated with a second value type. The thirdvalue may include a third weight value and the fourth value may includea fourth weight value. The first data object may further indicate afifth value corresponding to a third value type. The third value typemay correspond to an effort request for information type (e.g.,indicating an amount of effort required for performing various aspectsof the respective project).

In some embodiments, the systems and methods described herein may beconfigured to determine a first sum of a product of the first value andthe first weight value plus a product of the second value and the secondweight value. The systems and methods described herein may be configuredto generate a first score based on a result of the first sum divided bya sum of the first weight value and the second weight value.

In some embodiments, the systems and methods described herein may beconfigured to determine a second sum of a product of the third value andthe third weight value plus a product of the fourth value and the fourthweight value. The systems and methods described herein may be configuredto generate a second score based on a result of the second sum dividedby a sum of the third weight value and the fourth weight value.

In some embodiments, the first data object may further indicate a sixthvalue associated with a fourth value type and a seventh value associatedwith the fourth value type. The sixth value may include a fifth weightvalue and the seventh value may include a sixth weight value. Thesystems and methods described herein may be configured to determine athird sum of a product of the sixth value and the fifth weight valueplus a product of the seventh value and the sixth weight value. Thesystems and methods described herein may be configured to generate athird score based on a result of the third sum divided by a sum of thefifth weight value and the sixth weight value.

In some embodiments, systems and methods described herein may beconfigured to determine a first data object score (e.g., a common valuescore for the project corresponding to the first data object) for thefirst data object based on the first score, the second score, the thirdscore, and the fifth value. For example, the systems and methodsdescribed herein may be configured to determine a first sum of the firstscore, the second score, and the third score. The systems and methodsdescribed herein may be configured to generate the first data objectscore by determining the result of the first sum divided by the fifthvalue.

In some embodiments, the systems and methods described herein may beconfigured to generate a report including, at least, the first dataobject score and at least one other data object score. The first dataobject score and the at least one other data object may be organized onthe report according to a dynamically generated order. In someembodiments, the systems and methods described herein may be configuredto output, to a display, the report.

In some embodiments, the systems and methods described herein may beconfigured to perform evolutionary optimization to identify optimum orsubstantially optimum combinations of projects associated with dataobjects. The systems and methods described herein may be configured touse the common value scores for the respective data objects (e.g.,corresponding to respective projects), domain costs corresponding to therespective data objects, or any other suitable information for therespective data objects to identify all possible combinations of dataobjects (which may be referred to as seeds). For example, the systemsand methods described herein may be configured to generate a matrix ofbinary strings. Each binary string corresponds to a possible combinationof data objects and includes a plurality of 1s and 0s. Each 1 in abinary string indicates that a corresponding data object is included inthe binary string and each 0 in the binary string indicates that acorresponding data object is not included in the binary string.

In some embodiments, the systems and methods described herein may beconfigured to perform weight sort on a plurality of common value scorescorresponding to respective ones of a plurality of data objects. Thesystem and methods described herein may be configured to receive a depthvalue. The depth value may be provided by the user or other suitableuser. In some embodiments, the depth value may be a predefined value ormay be a value generated by the systems and methods described herein.The depth value may indicate a number of data objects in the pluralityof data objects to consider when generated the possible combinations ofdata objects (e.g., the possible combinations of data objects mayinclude all possible combinations of data objects indicated by the depthvalue).

The systems and methods described herein may be configured to identifydata object combinations of the possible data object combinations havinga value above a threshold. For example, the systems and methodsdescribed herein may be configured to determine a value realized foreach data object combination of the possible data object combinations.As is generally illustrated in FIG. 4A, a first data object combination402 may represent one data object combination of the possible dataobject combinations. As is generally illustrated, the data objectcombination 402 includes a depth value of 5 (e.g., considering 5 dataobjects). However, it should be understood that any suitable depth valuemay be used.

The data object 402 may be represented by a binary string, such as01000. The binary string may represent which data objects are includedin the data object combination 402. As is generally illustrated, dataobject 1 is not included in the data object combination 402, data object2 is included in the data object combination 402, data object 3 is notincluded in the data object combination 402, data object 4 is notincluded in the data object combination 402, and data object 5 is notincluded in the data object combination 402. Each data object includes avalue. For example, the data object 2 includes a value of $200. Thevalue may represent a total benefit (e.g., tangible or intangible)expected from performing the project associated with the data object.Additionally, or alternatively, each data object includes a weightvalue. For example, the data object 2 includes a weight value of $3. Theweight value may represent a total cost (e.g., tangle or intangible)associated with performing the project associated with the data object.

The data object combination 402 may include weight values associatedwith domains corresponding to the project associated with each dataobject. For example, the data object combination 402 may include adomain 1, a domain 2, and a domain 3. As described, a domain mayrepresent a resource or group or resources required to perform theproject associated with the data object. As is generally illustrated,the domain 1, the domain 2, and the domain 3 include a weight value of$1 for the data object 2. Correspondingly, the weight value for the dataobject 2 (e.g., and or any other data object) includes a sum of theweights of the domains. It should be understood that the valuesgenerally illustrated and described herein are for example purposes onlyand any suitable value may be used.

The systems and methods described herein may be configured to determinea total weight for a domain for the data object combination 402 bydetermining a sum of the domain weights for each data object included inthe data object combination 402. For example, the data object 402includes a total domain weight for domain 1 of $1 a total domain weightfor domain 2 of $1, and a total domain weight for domain 3 of $1.Additionally, or alternatively, the systems and methods described hereinmay be configured to determine a total value realized for a data objectcombination. As is generally illustrated, the value realized for thedata object combination 402 is $200.

As is generally illustrated in FIG. 4B, a data object 404 includes abinary string of 01011 (e.g., indicating that data object 2, data object4, and data object 5 are included in the data object combination 404).The data object combination 404 includes a total domain weight fordomain 1 of $1, a total domain weight for domain 2 of $4, and a totaldomain weight for domain 3 of $2. Additionally, or alternatively, thedata object combination 404 includes a total value realized of $1,100.

In some embodiments, the systems and methods described herein may beconfigured to generate a first set of data object combinations based ontotal domain weight values for each of the possible data objectcombinations. For example, the systems and methods described herein maybe configured to identify data object combinations having at least onetotal domain weight that is greater than threshold. The threshold mayinclude a percentage (e.g., 95% or other suitable value) of a domainbudget corresponding to the domain. If the systems and methods describedherein identify a data object combination having a total domain weightvalue that is greater than the threshold, the systems and methodsdescribed herein discard the data object combination. That is, thesystems and methods described herein may be configured to generate thefirst set of data object combinations using data object combinationshaving total domain weight values that are less than or equal to thethreshold. As is generally illustrated, the systems and methodsdescribed herein may include the data object combination 402 and thedata object combination 404 in the first set of data object combinations(e.g., because each of the total domain weight values is less than thethreshold).

In some embodiments, the systems and methods described herein may beconfigured to generate a second set of data object combination using thefirst set of data object combinations. For example, the systems andmethods described herein may be configured to determine a mutation ratevalue. The systems and methods described herein may be configured toreceive the mutation rate value from the user or other suitable user,identify the mutation rate value stored in a suitable memory, generate arandomly determined mutation rate value, or determine the mutation ratevalue using any suitable technique. The mutation rate value may indicatea rate at which 1s and 0s of binary strings of randomly selected dataobject combinations of the first set of data object combinations areflipped (e.g., is changed to 0s and 0s changed to 1s).

The systems and methods described herein may be configured to apply themutation rate value to the first set of data object combinations. Forexample, the systems and methods described herein may be configured toidentify, at random, a number of data object combinations of the firstset of data object combinations corresponding to the mutation rate value(e.g., a mutation rate value of 0.1 will result in the systems andmethods described herein randomly selecting 10% of the data objectcombinations from the first set of data object combustions). The systemsand methods described herein may be configured to flip is to 0s and 0sto is for binary strings corresponding to the selected data objectcombinations. The systems and methods described herein may be configuredto generate the second set of data object combinations using theselected data object combinations (e.g., having is change to 0s and 0schanged to is for corresponding binary strings) and the other (e.g.,90%) data object combinations of the first set of data objectcombinations.

In some embodiments, the system and methods described herein may beconfigured to identify core data objects (e.g., which may be referred toas core genes) and variable data objects (e.g., which may be referred toas variable genes). For example, the systems and methods describedherein may be configured to identify pairs of data object combinationsfrom the second set of data object combinations. As is generallyillustrated in FIG. 4C, the systems and methods described herein mayidentify a data object combination 1 and a data object combination 2.The systems and methods described herein may be configured to identifydata objects included in both the data object combination 1 and the dataobject combination 2. For example, the systems and methods describedherein may identify the data object 1, the data object 2, and so thatare indicated as being included in both the data object combination 1and the data object combination 2.

The systems and methods described herein may be configured to identifythe data objects included in both the data object combination 1 and thedata object combination 2 as core data objects. The systems and methodsdescribed herein may identify all other data objects (e.g., data objectsincluded in only one of the data object combination 1 and the dataobject combination 2). The systems and methods described herein may beconfigured to generate offspring data combinations that include allpossible data object combinations including all of the core data objectsand every possible combination of variable data objects.

In some embodiments, the systems and methods described herein mayiteratively identify, for a period corresponding to a search rate value,offspring data object combinations for other pairs of data objectcombinations from the second set of data object combinations. The searchrate value may be provided by the user or other suitable user orretrieved from a suitable memory. The search rate value may indicate alength of time, a number of cycles, and the like that the systems andmethods described herein may continue to identify offspring datacombinations.

The systems and methods described herein may be configured to determinea total domain weight for each domain associated with each offspringdata combination, as described. Additionally, or alternatively, thesystems and methods described herein may be configured to determine atotal value realized for each of the offspring data combinations.

In some embodiments, the systems and methods described herein may beconfigured to generate a third set of data object combinations based onthe offspring data combinations. For example, the systems and methodsdescribed herein may be configured to identify offspring datacombinations having at least one total domain weight value that isgreater than the threshold, as described. The systems and methodsdescribed herein may disregard offspring data object combinations thatincluded at least one total domain weight value that is greater than thethreshold.

The systems and methods described herein may be configured to identify,of the offspring data object combinations not disregarded, offspringdata object combinations having a total value realized greater than ahighest total value realized in the previously identified data objectcombinations (e.g. for all data object combinations of the first set ofdata object combinations). The systems and method described herein maybe configured to generate the third set of data object combinationsusing the offspring data object combinations not discarded that have atotal value realized greater than the highest total value realized inthe previously identified data object combinations.

The systems and methods described herein may continue to iterativelyidentify offspring data object combinations using the third set of dataobject combinations (e.g., and further generated sets of data objectcombinations) for a period corresponding to the search rate value orother suitable period. The systems and methods described herein may beconfigured to generate an output indicating a set of data objectcombinations corresponding to a final set of data object combinationsidentified by the systems and methods described herein. The final set ofdata object combinations may correspond to a set of optimal orsubstantially optimal data object combinations. The systems and methodsdescribed herein may be configured to provide the output, as described.

In some embodiments, the systems and methods described herein may beconfigured to receive a plurality of data objects. Each data object mayinclude a corresponding score value and a corresponding weight value.The systems and methods described herein may be configured to determine,using natural language processing of the plurality of data objects, atleast one resource domain. The at least one resource domain having aweight value and corresponding to resources utilized for a projectassociated with a corresponding data object of the plurality of dataobjects.

The systems and methods described herein may be configured to identifyall possible data object combinations for at least some (e.g., accordingto the depth value) of the plurality of data objects for the at leastone resource domain. Each data object combination may be represented bya binary data string indicating selected data objects for a respectivedata object combination. The systems and methods described herein may beconfigured to determine a total score value for each data objectcombination of the identified possible data object combinations bycalculating a sum of the corresponding score values for each data objectidentified in a respective data object combination of the identifiedpossible data object combinations.

The systems and methods described herein may be configured to determinea total weight value for each data object combination of the identifiedpossible data object combinations by calculating a sum of thecorresponding weight values for each data object identified in arespective data object combination of the identified possible dataobject combinations. The systems and methods described herein may beconfigured to identify data object combinations of the identifiedpossible data object combinations having a total weight value less thanor equal to the weight value of the at least one resource domain.

The systems and methods described herein may be configured to identifydata object combinations, of the identified data object combinations ofthe possible data object combinations having a total weight value lessthan or equal to the weight value of the at least one resource domain,having a total score value greater than a first total score valuethreshold.

The systems and methods described herein may be configured to generate afirst set of data object combinations using identified data objectcombinations, of the identified data object combinations of the possibledata object combinations having a total weight value less than or equalto the weight value of the at least one resource domain, having a totalscore value greater than the first total score value threshold.

In some embodiments, the systems and methods described herein may beconfigured to determine a plurality of total values for each data objectcombination of the identified possible data object combinations. Forexample, the systems and methods described herein may determine a totalcompliance value for each data object combination, a total weight valuefor each data object combination, a total score value for each dataobject combination, a total technology score value for each data objectcombination, other suitable values for each data combination, or acombination thereof.

In some embodiments, the systems and methods described herein may beconfigured to a benefit for each data object combination based on atheme corresponding to one or more of the plurality of total values. Thetheme may include a technology them, a compliance theme, a monetarytheme, and the like. The theme may include a threshold valuecorresponding to a subject matter of the theme. For example, atechnology theme may include a theme threshold of 40% (e.g., indicatingthat data object combinations at least 40% of corresponding data objectsdirected to technology projects are equal to or above the threshold).The systems and methods described herein may identify data objectcombinations having a theme value greater than the threshold. Forexample, the systems and methods described herein may use one or more ofthe plurality of total values to identify data object combinationshaving a total theme score that is greater than or equal to thethreshold.

In some embodiments, the systems and methods described herein mayidentify data object combinations of the identified data objectcombinations (e.g., having a total theme score greater than or equal tothe theme threshold), having a total value score greater than or equalto a value threshold. For example, the systems and methods describedherein may be configured to identify data object combinations having atleast 40% of corresponding data objects directed to technology projectsand having a total value score that is greater than or equal to amonetary value threshold. It should be understood that the systems andmethods described herein may be configured to identify the data objectcombinations using any suitable theme, value, information, and the like.

In some embodiments, the systems and methods described herein may beconfigured to set a mutation variable to a predetermine value. Thesystems and methods described herein may be configured to apply themutation variable to the first set of data object combinations. Thesystems and methods described herein may be configured to select atleast two data object combinations of the first set of data objectcombinations after application of the mutation variable to the first setof data object combinations.

In some embodiments, the systems and methods described herein may beconfigured to identify data objects that appear in each of the at leasttwo data object combinations of the first set of data objectcombinations. In some embodiments, the at least two data objectcombinations may be identified at random, may be identified based on areward function of weight objectives, identified based on one or more ofthe plurality of total values, identified using other suitableinformation, or a combination thereof. The systems and methods describedherein may be configured to generate, using an artificial intelligenceengine configured to use at least one machine learning model configuredto identify data object combinations, a second set of data objectcombinations using the data objects that appear in each of the at leasttwo data object combinations of the first set of data objectcombinations and each possible combination of data objects that do notappear in at least one data object combination of the at least two dataobject combinations of the first set of data object combinations.

In some embodiments, the systems and methods described herein may beconfigured to provide, to a display, output indicating data objectcombinations of the second set of data object combinations having atotal score value above a second total score value threshold.

In some embodiments, the systems and methods described herein may beconfigured to trained artificial neural network for providingsuggestions of impacted technical teams (e.g., which may be referred toherein as resource domains) for a project description provided in acorresponding data object. The artificial neural network may include amulti-layer perceptron (MLP). The artificial neural network may includea collection of connected units or nodes called artificial neurons,(e.g., which model the neurons in a biological brain). The neurons maybe configured to receive and learn from (e.g., or train on) particularinformation.

MLP architecture may include at least three layers of nodes. Forexample, the layers of nodes may include an input layer, a hidden layer,and an output layer. Except for the input nodes, each node is a neuronthat may use a nonlinear activation function. The MLP architecture mayutilize a supervised learning technique. The supervised learningtechnique may include a backpropagation (e.g., for training) and/orother suitable technique. The MLP architecture may be configured todistinguish data that is not linearly separable.

In some embodiments, the systems and methods described herein may beconfigure to provide, as input to the artificial neural network, a freeform text. The free form text may correspond to a description field ofthe data object. The description field may correspond or include adescription of the project associated with the data object. It should beunderstood that the systems and methods described herein may beconfigured to use any suitable data object and/or free form text inaddition to and/or other than those described herein.

In some embodiments, the systems and methods described herein may beconfigured to convert the free from text input to numeric format. Forexample, the systems and methods described herein may be configured toidentify stop words and to filter the stop words from the free form text(e.g., which may leave only important words in the free form text). Stopwords may include a set of most commonly used words in a language, suchas, in the English language, a, the, an, is, and the like.

The systems and methods described herein may be configured to convertthe filtered free form text to a set of feature vectors. A vector mayinclude a series of numbers (e.g., similar to a matrix) with one columnand multiple rows. The vector may be represented spatially. A featuremay include a numerical or symbolic property of an aspect of the dataobject. Accordingly, a feature vector may include a vector that includesmultiple elements of a data object.

In some embodiments, the features described herein may include acollection of one, two, or three word phrases formed from the free formtext input. The one, two, and three word phrases may be referred to asunigrams, bigrams, and trigrams, respectively. In some embodiments, thefeature vectors may include 10000 of such features, selected based on aterm frequency-inverse document frequency (Tf-IDF) score. The Tf-IDFscore may include a numeric value that represents how important afeature is in the context of historical data (e.g., project descriptionsassociated with respective data objects) used for building and/ortraining the MLP. The systems and methods described herein may beconfigured to provide, as input to the MLP for training, the free formtext after converting the free form text into a feature vector with anumber of features (e.g., such as 10000 features or other suitablenumber of features).

In some embodiments, machine learning model may include a three-layerMLP network, as is generally illustrated in FIG. 8. The MLP network mayinclude with first layer 802, a second layer 804, and a third layer 806.The first layer 802 may include an input layer containing apredetermined number of nodes (e.g., neurons), such as 10000 nodes orother suitable number of nodes. Each of the nodes in the first layer 802may correspond to each feature of the feature vector, as described.

In some embodiment, the second layer 804 may include a hidden layer thatcontains a predetermined number of nodes, such as 1000 nodes or othersuitable number of nodes. The third layer 806 may include an outputlayer containing a predetermined number of nodes, such as 515 nodes orother suitable number of nodes. Each node of the third layer 806 mayrepresent a different resource domain (e.g., which may be referred to asa target variable) associated with the data used to train the machinelearning model.

In some embodiments, the MLP network may include a fully-connectedmulti-layer perceptron. For example, each node in a respective layer ofthe MLP network is connected with each node in a respective previouslayer and a respective next layer. The systems and methods describedherein may be configured to apply activation functions to the output ofevery layer in the MLP before the output is provide as input to the nextlayer. In some embodiments, the systems and methods described herein maybe configured to use a rectified linear unit (ReLU) activation functionfor each of the first layer 802 and the second layer 804, which may notactivate all the neurons at the same time. This reduce overfitting andmay improve model efficiency.

In some embodiments, the systems and methods described herein may beconfigured to use a probabilistic activation function for the thirdlayer 806, which may generate an output that includes a probabilityscore, ranging from 0 to 1 (e.g., or other suitable range), orlikelihood of occurrence. For example, the output of each of the 515nodes in third layer 806, includes a probability of that a node (e.g.,and corresponding resource domain) to be a likely suggestion. In someembodiments, the systems and methods described herein may be configuredto use the machine learning model to output a plurality of predictions(e.g., such as 515 (one for each node) or other suitable number ofpredictions). Each prediction may include a resource domain and aweight. As described, the weight may indicating the likelihood that thecorresponding resource domain is implicated by (e.g., the resourcedomain is required for fulfilling the project objectives described indata object) the data object. Weights have a greater value are morelikely to be implicated by the data object than weights having a lowervalue.

The systems and methods described herein may be configured to use asigmoid activation function for the third layer 806, as multipleresource domains may be implicated for free form text associated with adata object (e.g., which may be referred to as multi-labelclassification).

In some embodiments, each individual node in the machine learning modelmay include a mathematical unit that receives multiple values as inputeither from a previous layer or an input layer. Each connectionconnecting a node to other nodes in the machine learning model mayinclude weight values. The systems and methods described herein may beconfigured to combine these inputs using the following equation:

Σ=X1×W1+X2×W2+X3×W3+ . . . +Xn×Wn

Where, X1, X2, X3 . . . Xn correspond to respective input values and W1,W2, W3 . . . Wn correspond to weight values for each respective inputvalue. The systems and methods described herein may be configured to, inresponse to the combining the input values, apply an activationfunction. The activation function, as described, includes an equationthat may normalize the output of each layer to a particular range. Theseranges vary for different activation functions (e.g., 0 to 1, −1 to 1,and the like). In some embodiments, the systems and methods describedherein may use the ReLU activation function to normalize the output to 0if Σ<0, and it will not change if Σ>=0. The third layer 806 may use asigmoid activation layer, which normalizes any input to range between 0and 1.

The weights are optimized during the training phase by a method calledbackpropagation. Backpropagation, essentially, adjusts the weights basedon error calculated during training using an error function. Errorfunction used for our case is the Cross-entropy error function.

In some embodiments, the systems and methods described herein may beconfigured to train the machine learning model using historical dataobjects (e.g., and corresponding project descriptions). In someembodiments, the systems and methods described herein may be configuredto transform the historical free form text of the historical dataobjects into feature vectors, as described. The nodes of the machinelearning model may include an associated weight. During training, themachine learning model may iterate through the feature vectors multipletimes, each time modifying these weights based on an error score. Errorscores may be calculated using a mathematical function, such as a lossfunction or other suitable function. For example, systems and methodsdescribed herein may be configured to calculate the error scores using across-entropy loss function. The systems and methods described hereinmay be configured to modify the weights until a certain accuracy levelis reached, for example, a 78% accuracy level or other suitable accuracylevel.

In some embodiments, the systems and methods described herein may beconfigured to receive at least one data object. The systems and methodsdescribed herein may be configured to identify, for a first aspect ofthe at least one data object that includes free form textualinformation, text strings of the free form textual information having afirst text string type. The systems and methods described herein may beconfigured to, in response to identifying at least one text string ofthe free form textual information having the first text string type,generate updated free form textual information by removing the at leastone text string having the first text string type. The systems andmethods described herein may be configured to generate one or morefeature vectors based on the updated free form textual information usingat least one of a unigram, a bigram, and a trigram.

The systems and methods described herein may be configured to use anartificial intelligence engine that uses at least one machine learningmodel configured to provide, using the one or more feature vectors, anoutput that includes at least one prediction indicating at least oneresource domain and a weight value indicating a probability that the atleast one resource domain corresponds to the free form textualinformation. The systems and methods described herein may be configuredto provide, at a display, the output.

In some embodiments, the at least one machine learning model includes amulti-layer perceptron model. In some embodiments, the at least onemachine learning model includes a fully-connected multi-layer perceptronmodel. In some embodiments, the at least one machine learning modelincludes at least an input layer, a hidden layer, and an output layer.In some embodiments, nodes associated with the hidden layer and theoutput layer use a non-linear activation function. In some embodiments,the machine learning model is initially trained using a supervisedlearning technique. In some embodiments, the supervised learningtechnique incudes backpropagation. In some embodiments, the machinelearning model is iteratively trained using at least the output of theat least one machine learning model. In some embodiments, the one ormore feature vectors include a term frequency-inverse document frequencyscore.

FIG. 1A is a block diagram of an example implementation of a system 100for a high-volume pharmacy. While the system 100 is generally describedas being deployed in a high-volume pharmacy or a fulfillment center (forexample, a mail order pharmacy, a direct delivery pharmacy, etc.), thesystem 100 and/or components of the system 100 may otherwise be deployed(for example, in a lower-volume pharmacy, etc.). A high-volume pharmacymay be a pharmacy that is capable of filling at least some prescriptionsmechanically. The system 100 may include a benefit manager device 102and a pharmacy device 106 in communication with each other directlyand/or over a network 104. The system 100 may also include a storagedevice 110.

The benefit manager device 102 is a device operated by an entity that isat least partially responsible for creation and/or management of thepharmacy or drug benefit. While the entity operating the benefit managerdevice 102 is typically a pharmacy benefit manager (PBM), other entitiesmay operate the benefit manager device 102 on behalf of themselves orother entities (such as PBMs). For example, the benefit manager device102 may be operated by a health plan, a retail pharmacy chain, a drugwholesaler, a data analytics or other type of software-related company,etc. In some implementations, a PBM that provides the pharmacy benefitmay provide one or more additional benefits including a medical orhealth benefit, a dental benefit, a vision benefit, a wellness benefit,a radiology benefit, a pet care benefit, an insurance benefit, a longterm care benefit, a nursing home benefit, etc. The PBM may, in additionto its PBM operations, operate one or more pharmacies. The pharmaciesmay be retail pharmacies, mail order pharmacies, etc.

Some of the operations of the PBM that operates the benefit managerdevice 102 may include the following activities and processes. A member(or a person on behalf of the member) of a pharmacy benefit plan mayobtain a prescription drug at a retail pharmacy location (e.g., alocation of a physical store) from a pharmacist or a pharmacisttechnician. The member may also obtain the prescription drug throughmail order drug delivery from a mail order pharmacy location, such asthe system 100. In some implementations, the member may obtain theprescription drug directly or indirectly through the use of a machine,such as a kiosk, a vending unit, a mobile electronic device, or adifferent type of mechanical device, electrical device, electroniccommunication device, and/or computing device. Such a machine may befilled with the prescription drug in prescription packaging, which mayinclude multiple prescription components, by the system 100. Thepharmacy benefit plan is administered by or through the benefit managerdevice 102.

The member may have a copayment for the prescription drug that reflectsan amount of money that the member is responsible to pay the pharmacyfor the prescription drug. The money paid by the member to the pharmacymay come from, as examples, personal funds of the member, a healthsavings account (HSA) of the member or the member's family, a healthreimbursement arrangement (HRA) of the member or the member's family, ora flexible spending account (FSA) of the member or the member's family.In some instances, an employer of the member may directly or indirectlyfund or reimburse the member for the copayments.

The amount of the copayment required by the member may vary acrossdifferent pharmacy benefit plans having different plan sponsors orclients and/or for different prescription drugs. The member's copaymentmay be a flat copayment (in one example, $10), coinsurance (in oneexample, 10%), and/or a deductible (for example, responsibility for thefirst $500 of annual prescription drug expense, etc.) for certainprescription drugs, certain types and/or classes of prescription drugs,and/or all prescription drugs. The copayment may be stored in thestorage device 110 or determined by the benefit manager device 102.

In some instances, the member may not pay the copayment or may only paya portion of the copayment for the prescription drug. For example, if ausual and customary cost for a generic version of a prescription drug is$4, and the member's flat copayment is $20 for the prescription drug,the member may only need to pay $4 to receive the prescription drug. Inanother example involving a worker's compensation claim, no copaymentmay be due by the member for the prescription drug.

In addition, copayments may also vary based on different deliverychannels for the prescription drug. For example, the copayment forreceiving the prescription drug from a mail order pharmacy location maybe less than the copayment for receiving the prescription drug from aretail pharmacy location.

In conjunction with receiving a copayment (if any) from the member anddispensing the prescription drug to the member, the pharmacy submits aclaim to the PBM for the prescription drug. After receiving the claim,the PBM (such as by using the benefit manager device 102) may performcertain adjudication operations including verifying eligibility for themember, identifying/reviewing an applicable formulary for the member todetermine any appropriate copayment, coinsurance, and deductible for theprescription drug, and performing a drug utilization review (DUR) forthe member. Further, the PBM may provide a response to the pharmacy (forexample, the pharmacy system 100) following performance of at least someof the aforementioned operations.

As part of the adjudication, a plan sponsor (or the PBM on behalf of theplan sponsor) ultimately reimburses the pharmacy for filling theprescription drug when the prescription drug was successfullyadjudicated. The aforementioned adjudication operations generally occurbefore the copayment is received and the prescription drug is dispensed.However in some instances, these operations may occur simultaneously,substantially simultaneously, or in a different order. In addition, moreor fewer adjudication operations may be performed as at least part ofthe adjudication process.

The amount of reimbursement paid to the pharmacy by a plan sponsorand/or money paid by the member may be determined at least partiallybased on types of pharmacy networks in which the pharmacy is included.In some implementations, the amount may also be determined based onother factors. For example, if the member pays the pharmacy for theprescription drug without using the prescription or drug benefitprovided by the PBM, the amount of money paid by the member may behigher than when the member uses the prescription or drug benefit. Insome implementations, the amount of money received by the pharmacy fordispensing the prescription drug and for the prescription drug itselfmay be higher than when the member uses the prescription or drugbenefit. Some or all of the foregoing operations may be performed byexecuting instructions stored in the benefit manager device 102 and/oran additional device.

Examples of the network 104 include a Global System for MobileCommunications (GSM) network, a code division multiple access (CDMA)network, 3rd Generation Partnership Project (3GPP), an Internet Protocol(IP) network, a Wireless Application Protocol (WAP) network, or an IEEE802.11 standards network, as well as various combinations of the abovenetworks. The network 104 may include an optical network. The network104 may be a local area network or a global communication network, suchas the Internet. In some implementations, the network 104 may include anetwork dedicated to prescription orders: a prescribing network such asthe electronic prescribing network operated by Surescripts of Arlington,Va.

Moreover, although the system shows a single network 104, multiplenetworks can be used. The multiple networks may communicate in seriesand/or parallel with each other to link the devices 102-110.

The pharmacy device 106 may be a device associated with a retailpharmacy location (e.g., an exclusive pharmacy location, a grocery storewith a retail pharmacy, or a general sales store with a retail pharmacy)or other type of pharmacy location at which a member attempts to obtaina prescription. The pharmacy may use the pharmacy device 106 to submitthe claim to the PBM for adjudication.

Additionally, in some implementations, the pharmacy device 106 mayenable information exchange between the pharmacy and the PBM. Forexample, this may allow the sharing of member information such as drughistory that may allow the pharmacy to better service a member (forexample, by providing more informed therapy consultation and druginteraction information). In some implementations, the benefit managerdevice 102 may track prescription drug fulfillment and/or otherinformation for users that are not members, or have not identifiedthemselves as members, at the time (or in conjunction with the time) inwhich they seek to have a prescription filled at a pharmacy.

The pharmacy device 106 may include a pharmacy fulfillment device 112,an order processing device 114, and a pharmacy management device 116 incommunication with each other directly and/or over the network 104. Theorder processing device 114 may receive information regarding fillingprescriptions and may direct an order component to one or more devicesof the pharmacy fulfillment device 112 at a pharmacy. The pharmacyfulfillment device 112 may fulfill, dispense, aggregate, and/or pack theorder components of the prescription drugs in accordance with one ormore prescription orders directed by the order processing device 114.

In general, the order processing device 114 is a device located withinor otherwise associated with the pharmacy to enable the pharmacyfulfilment device 112 to fulfill a prescription and dispenseprescription drugs. In some implementations, the order processing device114 may be an external order processing device separate from thepharmacy and in communication with other devices located within thepharmacy.

For example, the external order processing device may communicate withan internal pharmacy order processing device and/or other deviceslocated within the system 100. In some implementations, the externalorder processing device may have limited functionality (e.g., asoperated by a user requesting fulfillment of a prescription drug), whilethe internal pharmacy order processing device may have greaterfunctionality (e.g., as operated by a pharmacist).

The order processing device 114 may track the prescription order as itis fulfilled by the pharmacy fulfillment device 112. The prescriptionorder may include one or more prescription drugs to be filled by thepharmacy. The order processing device 114 may make pharmacy routingdecisions and/or order consolidation decisions for the particularprescription order. The pharmacy routing decisions include whatdevice(s) in the pharmacy are responsible for filling or otherwisehandling certain portions of the prescription order. The orderconsolidation decisions include whether portions of one prescriptionorder or multiple prescription orders should be shipped together for auser or a user family. The order processing device 114 may also trackand/or schedule literature or paperwork associated with eachprescription order or multiple prescription orders that are beingshipped together. In some implementations, the order processing device114 may operate in combination with the pharmacy management device 116.

The order processing device 114 may include circuitry, a processor, amemory to store data and instructions, and communication functionality.The order processing device 114 is dedicated to performing processes,methods, and/or instructions described in this application. Other typesof electronic devices may also be used that are specifically configuredto implement the processes, methods, and/or instructions described infurther detail below.

In some implementations, at least some functionality of the orderprocessing device 114 may be included in the pharmacy management device116. The order processing device 114 may be in a client-serverrelationship with the pharmacy management device 116, in a peer-to-peerrelationship with the pharmacy management device 116, or in a differenttype of relationship with the pharmacy management device 116. The orderprocessing device 114 and/or the pharmacy management device 116 maycommunicate directly (for example, such as by using a local storage)and/or through the network 104 (such as by using a cloud storageconfiguration, software as a service, etc.) with the storage device 110.

The storage device 110 may include: non-transitory storage (for example,memory, hard disk, CD-ROM, etc.) in communication with the benefitmanager device 102 and/or the pharmacy device 106 directly and/or overthe network 104. The non-transitory storage may store order data 118,member data 120, claims data 122, drug data 124, prescription data 126,and/or plan sponsor data 128. Further, the system 100 may includeadditional devices, which may communicate with each other directly orover the network 104.

The order data 118 may be related to a prescription order. The orderdata may include type of the prescription drug (for example, drug nameand strength) and quantity of the prescription drug. The order data 118may also include data used for completion of the prescription, such asprescription materials. In general, prescription materials include anelectronic copy of information regarding the prescription drug forinclusion with or otherwise in conjunction with the fulfilledprescription. The prescription materials may include electronicinformation regarding drug interaction warnings, recommended usage,possible side effects, expiration date, date of prescribing, etc. Theorder data 118 may be used by a high-volume fulfillment center tofulfill a pharmacy order.

In some implementations, the order data 118 includes verificationinformation associated with fulfillment of the prescription in thepharmacy. For example, the order data 118 may include videos and/orimages taken of (i) the prescription drug prior to dispensing, duringdispensing, and/or after dispensing, (ii) the prescription container(for example, a prescription container and sealing lid, prescriptionpackaging, etc.) used to contain the prescription drug prior todispensing, during dispensing, and/or after dispensing, (iii) thepackaging and/or packaging materials used to ship or otherwise deliverthe prescription drug prior to dispensing, during dispensing, and/orafter dispensing, and/or (iv) the fulfillment process within thepharmacy. Other types of verification information such as barcode dataread from pallets, bins, trays, or carts used to transport prescriptionswithin the pharmacy may also be stored as order data 118.

The member data 120 includes information regarding the membersassociated with the PBM. The information stored as member data 120 mayinclude personal information, personal health information, protectedhealth information, etc. Examples of the member data 120 include name,address, telephone number, e-mail address, prescription drug history,etc. The member data 120 may include a plan sponsor identifier thatidentifies the plan sponsor associated with the member and/or a memberidentifier that identifies the member to the plan sponsor. The memberdata 120 may include a member identifier that identifies the plansponsor associated with the user and/or a user identifier thatidentifies the user to the plan sponsor. The member data 120 may alsoinclude dispensation preferences such as type of label, type of cap,message preferences, language preferences, etc.

The member data 120 may be accessed by various devices in the pharmacy(for example, the high-volume fulfillment center, etc.) to obtaininformation used for fulfillment and shipping of prescription orders. Insome implementations, an external order processing device operated by oron behalf of a member may have access to at least a portion of themember data 120 for review, verification, or other purposes.

In some implementations, the member data 120 may include information forpersons who are users of the pharmacy but are not members in thepharmacy benefit plan being provided by the PBM. For example, theseusers may obtain drugs directly from the pharmacy, through a privatelabel service offered by the pharmacy, the high-volume fulfillmentcenter, or otherwise. In general, the use of the terms “member” and“user” may be used interchangeably.

The claims data 122 includes information regarding pharmacy claimsadjudicated by the PBM under a drug benefit program provided by the PBMfor one or more plan sponsors. In general, the claims data 122 includesan identification of the client that sponsors the drug benefit programunder which the claim is made, and/or the member that purchased theprescription drug giving rise to the claim, the prescription drug thatwas filled by the pharmacy (e.g., the national drug code number, etc.),the dispensing date, generic indicator, generic product identifier (GPI)number, medication class, the cost of the prescription drug providedunder the drug benefit program, the copayment/coinsurance amount, rebateinformation, and/or member eligibility, etc. Additional information maybe included.

In some implementations, other types of claims beyond prescription drugclaims may be stored in the claims data 122. For example, medicalclaims, dental claims, wellness claims, or other types ofhealth-care-related claims for members may be stored as a portion of theclaims data 122.

In some implementations, the claims data 122 includes claims thatidentify the members with whom the claims are associated. Additionallyor alternatively, the claims data 122 may include claims that have beende-identified (that is, associated with a unique identifier but not witha particular, identifiable member).

The drug data 124 may include drug name (e.g., technical name and/orcommon name), other names by which the drug is known, activeingredients, an image of the drug (such as in pill form), etc. The drugdata 124 may include information associated with a single medication ormultiple medications.

The prescription data 126 may include information regardingprescriptions that may be issued by prescribers on behalf of users, whomay be members of the pharmacy benefit plan—for example, to be filled bya pharmacy. Examples of the prescription data 126 include user names,medication or treatment (such as lab tests), dosing information, etc.The prescriptions may include electronic prescriptions or paperprescriptions that have been scanned. In some implementations, thedosing information reflects a frequency of use (e.g., once a day, twicea day, before each meal, etc.) and a duration of use (e.g., a few days,a week, a few weeks, a month, etc.).

In some implementations, the order data 118 may be linked to associatedmember data 120, claims data 122, drug data 124, and/or prescriptiondata 126.

The plan sponsor data 128 includes information regarding the plansponsors of the PBM. Examples of the plan sponsor data 128 includecompany name, company address, contact name, contact telephone number,contact e-mail address, etc.

FIG. 2 illustrates the pharmacy fulfillment device 112 according to anexample implementation. The pharmacy fulfillment device 112 may be usedto process and fulfill prescriptions and prescription orders. Afterfulfillment, the fulfilled prescriptions are packed for shipping.

The pharmacy fulfillment device 112 may include devices in communicationwith the benefit manager device 102, the order processing device 114,and/or the storage device 110, directly or over the network 104.Specifically, the pharmacy fulfillment device 112 may include palletsizing and pucking device(s) 206, loading device(s) 208, inspectdevice(s) 210, unit of use device(s) 212, automated dispensing device(s)214, manual fulfillment device(s) 216, review devices 218, imagingdevice(s) 220, cap device(s) 222, accumulation devices 224, packingdevice(s) 226, literature device(s) 228, unit of use packing device(s)230, and mail manifest device(s) 232. Further, the pharmacy fulfillmentdevice 112 may include additional devices, which may communicate witheach other directly or over the network 104.

In some implementations, operations performed by one of these devices206-232 may be performed sequentially, or in parallel with theoperations of another device as may be coordinated by the orderprocessing device 114. In some implementations, the order processingdevice 114 tracks a prescription with the pharmacy based on operationsperformed by one or more of the devices 206-232.

In some implementations, the pharmacy fulfillment device 112 maytransport prescription drug containers, for example, among the devices206-232 in the high-volume fulfillment center, by use of pallets. Thepallet sizing and pucking device 206 may configure pucks in a pallet. Apallet may be a transport structure for a number of prescriptioncontainers, and may include a number of cavities. A puck may be placedin one or more than one of the cavities in a pallet by the pallet sizingand pucking device 206. The puck may include a receptacle sized andshaped to receive a prescription container. Such containers may besupported by the pucks during carriage in the pallet. Different pucksmay have differently sized and shaped receptacles to accommodatecontainers of differing sizes, as may be appropriate for differentprescriptions.

The arrangement of pucks in a pallet may be determined by the orderprocessing device 114 based on prescriptions that the order processingdevice 114 decides to launch. The arrangement logic may be implementeddirectly in the pallet sizing and pucking device 206. Once aprescription is set to be launched, a puck suitable for the appropriatesize of container for that prescription may be positioned in a pallet bya robotic arm or pickers. The pallet sizing and pucking device 206 maylaunch a pallet once pucks have been configured in the pallet.

The loading device 208 may load prescription containers into the puckson a pallet by a robotic arm, a pick and place mechanism (also referredto as pickers), etc. In various implementations, the loading device 208has robotic arms or pickers to grasp a prescription container and moveit to and from a pallet or a puck. The loading device 208 may also printa label that is appropriate for a container that is to be loaded ontothe pallet, and apply the label to the container. The pallet may belocated on a conveyor assembly during these operations (e.g., at thehigh-volume fulfillment center, etc.).

The inspect device 210 may verify that containers in a pallet arecorrectly labeled and in the correct spot on the pallet. The inspectdevice 210 may scan the label on one or more containers on the pallet.Labels of containers may be scanned or imaged in full or in part by theinspect device 210. Such imaging may occur after the container has beenlifted out of its puck by a robotic arm, picker, etc., or may beotherwise scanned or imaged while retained in the puck. In someimplementations, images and/or video captured by the inspect device 210may be stored in the storage device 110 as order data 118.

The unit of use device 212 may temporarily store, monitor, label, and/ordispense unit of use products. In general, unit of use products areprescription drug products that may be delivered to a user or memberwithout being repackaged at the pharmacy. These products may includepills in a container, pills in a blister pack, inhalers, etc.Prescription drug products dispensed by the unit of use device 212 maybe packaged individually or collectively for shipping, or may be shippedin combination with other prescription drugs dispensed by other devicesin the high-volume fulfillment center.

At least some of the operations of the devices 206-232 may be directedby the order processing device 114. For example, the manual fulfillmentdevice 216, the review device 218, the automated dispensing device 214,and/or the packing device 226, etc. may receive instructions provided bythe order processing device 114.

The automated dispensing device 214 may include one or more devices thatdispense prescription drugs or pharmaceuticals into prescriptioncontainers in accordance with one or multiple prescription orders. Ingeneral, the automated dispensing device 214 may include mechanical andelectronic components with, in some implementations, software and/orlogic to facilitate pharmaceutical dispensing that would otherwise beperformed in a manual fashion by a pharmacist and/or pharmacisttechnician. For example, the automated dispensing device 214 may includehigh-volume fillers that fill a number of prescription drug types at arapid rate and blister pack machines that dispense and pack drugs into ablister pack. Prescription drugs dispensed by the automated dispensingdevices 214 may be packaged individually or collectively for shipping,or may be shipped in combination with other prescription drugs dispensedby other devices in the high-volume fulfillment center.

The manual fulfillment device 216 controls how prescriptions aremanually fulfilled. For example, the manual fulfillment device 216 mayreceive or obtain a container and enable fulfillment of the container bya pharmacist or pharmacy technician. In some implementations, the manualfulfillment device 216 provides the filled container to another devicein the pharmacy fulfillment devices 112 to be joined with othercontainers in a prescription order for a user or member.

In general, manual fulfillment may include operations at least partiallyperformed by a pharmacist or a pharmacy technician. For example, aperson may retrieve a supply of the prescribed drug, may make anobservation, may count out a prescribed quantity of drugs and place theminto a prescription container, etc. Some portions of the manualfulfillment process may be automated by use of a machine. For example,counting of capsules, tablets, or pills may be at least partiallyautomated (such as through use of a pill counter). Prescription drugsdispensed by the manual fulfillment device 216 may be packagedindividually or collectively for shipping, or may be shipped incombination with other prescription drugs dispensed by other devices inthe high-volume fulfillment center.

The review device 218 may process prescription containers to be reviewedby a pharmacist for proper pill count, exception handling, prescriptionverification, etc. Fulfilled prescriptions may be manually reviewedand/or verified by a pharmacist, as may be required by state or locallaw. A pharmacist or other licensed pharmacy person who may dispensecertain drugs in compliance with local and/or other laws may operate thereview device 218 and visually inspect a prescription container that hasbeen filled with a prescription drug. The pharmacist may review, verify,and/or evaluate drug quantity, drug strength, and/or drug interactionconcerns, or otherwise perform pharmacist services. The pharmacist mayalso handle containers which have been flagged as an exception, such ascontainers with unreadable labels, containers for which the associatedprescription order has been canceled, containers with defects, etc. Inan example, the manual review can be performed at a manual reviewstation.

The imaging device 220 may image containers once they have been filledwith pharmaceuticals. The imaging device 220 may measure a fill heightof the pharmaceuticals in the container based on the obtained image todetermine if the container is filled to the correct height given thetype of pharmaceutical and the number of pills in the prescription.Images of the pills in the container may also be obtained to detect thesize of the pills themselves and markings thereon. The images may betransmitted to the order processing device 114 and/or stored in thestorage device 110 as part of the order data 118.

The cap device 222 may be used to cap or otherwise seal a prescriptioncontainer. In some implementations, the cap device 222 may secure aprescription container with a type of cap in accordance with a userpreference (e.g., a preference regarding child resistance, etc.), a plansponsor preference, a prescriber preference, etc. The cap device 222 mayalso etch a message into the cap, although this process may be performedby a subsequent device in the high-volume fulfillment center.

The accumulation device 224 accumulates various containers ofprescription drugs in a prescription order. The accumulation device 224may accumulate prescription containers from various devices or areas ofthe pharmacy. For example, the accumulation device 224 may accumulateprescription containers from the unit of use device 212, the automateddispensing device 214, the manual fulfillment device 216, and the reviewdevice 218. The accumulation device 224 may be used to group theprescription containers prior to shipment to the member.

The literature device 228 prints, or otherwise generates, literature toinclude with each prescription drug order. The literature may be printedon multiple sheets of substrates, such as paper, coated paper, printablepolymers, or combinations of the above substrates. The literatureprinted by the literature device 228 may include information required toaccompany the prescription drugs included in a prescription order, otherinformation related to prescription drugs in the order, financialinformation associated with the order (for example, an invoice or anaccount statement), etc.

In some implementations, the literature device 228 folds or otherwiseprepares the literature for inclusion with a prescription drug order(e.g., in a shipping container). In other implementations, theliterature device 228 prints the literature and is separate from anotherdevice that prepares the printed literature for inclusion with aprescription order.

The packing device 226 packages the prescription order in preparationfor shipping the order. The packing device 226 may box, bag, orotherwise package the fulfilled prescription order for delivery. Thepacking device 226 may further place inserts (e.g., literature or otherpapers, etc.) into the packaging received from the literature device228. For example, bulk prescription orders may be shipped in a box,while other prescription orders may be shipped in a bag, which may be awrap seal bag.

The packing device 226 may label the box or bag with an address and arecipient's name. The label may be printed and affixed to the bag orbox, be printed directly onto the bag or box, or otherwise associatedwith the bag or box. The packing device 226 may sort the box or bag formailing in an efficient manner (e.g., sort by delivery address, etc.).The packing device 226 may include ice or temperature sensitive elementsfor prescriptions that are to be kept within a temperature range duringshipping (for example, this may be necessary in order to retainefficacy). The ultimate package may then be shipped through postal mail,through a mail order delivery service that ships via ground and/or air(e.g., UPS, FEDEX, or DHL, etc.), through a delivery service, through alocker box at a shipping site (e.g., AMAZON locker or a PO Box, etc.),or otherwise.

The unit of use packing device 230 packages a unit of use prescriptionorder in preparation for shipping the order. The unit of use packingdevice 230 may include manual scanning of containers to be bagged forshipping to verify each container in the order. In an exampleimplementation, the manual scanning may be performed at a manualscanning station. The pharmacy fulfillment device 112 may also include amail manifest device 232 to print mailing labels used by the packingdevice 226 and may print shipping manifests and packing lists.

While the pharmacy fulfillment device 112 in FIG. 2 is shown to includesingle devices 206-232, multiple devices may be used. When multipledevices are present, the multiple devices may be of the same device typeor models, or may be a different device type or model. The types ofdevices 206-232 shown in FIG. 2 are example devices. In otherconfigurations of the system 100, lesser, additional, or different typesof devices may be included.

Moreover, multiple devices may share processing and/or memory resources.The devices 206-232 may be located in the same area or in differentlocations. For example, the devices 206-232 may be located in a buildingor set of adjoining buildings. The devices 206-232 may be interconnected(such as by conveyors), networked, and/or otherwise in contact with oneanother or integrated with one another (e.g., at the high-volumefulfillment center, etc.). In addition, the functionality of a devicemay be split among a number of discrete devices and/or combined withother devices.

FIG. 3 illustrates the order processing device 114 according to anexample implementation. The order processing device 114 may be used byone or more operators to generate prescription orders, make routingdecisions, make prescription order consolidation decisions, trackliterature with the system 100, and/or view order status and other orderrelated information. For example, the prescription order may includeorder components.

The order processing device 114 may receive instructions to fulfill anorder without operator intervention. An order component may include aprescription drug fulfilled by use of a container through the system100. The order processing device 114 may include an order verificationsubsystem 302, an order control subsystem 304, and/or an order trackingsubsystem 306. Other subsystems may also be included in the orderprocessing device 114.

The order verification subsystem 302 may communicate with the benefitmanager device 102 to verify the eligibility of the member and reviewthe formulary to determine appropriate copayment, coinsurance, anddeductible for the prescription drug and/or perform a DUR (drugutilization review). Other communications between the order verificationsubsystem 302 and the benefit manager device 102 may be performed for avariety of purposes.

The order control subsystem 304 controls various movements of thecontainers and/or pallets along with various filling functions duringtheir progression through the system 100. In some implementations, theorder control subsystem 304 may identify the prescribed drug in one ormore than one prescription orders as capable of being fulfilled by theautomated dispensing device 214. The order control subsystem 304 maydetermine which prescriptions are to be launched and may determine thata pallet of automated-fill containers is to be launched.

The order control subsystem 304 may determine that an automated-fillprescription of a specific pharmaceutical is to be launched and mayexamine a queue of orders awaiting fulfillment for other prescriptionorders, which will be filled with the same pharmaceutical. The ordercontrol subsystem 304 may then launch orders with similar automated-fillpharmaceutical needs together in a pallet to the automated dispensingdevice 214. As the devices 206-232 may be interconnected by a system ofconveyors or other container movement systems, the order controlsubsystem 304 may control various conveyors: for example, to deliver thepallet from the loading device 208 to the manual fulfillment device 216from the literature device 228, paperwork as needed to fill theprescription.

The order tracking subsystem 306 may track a prescription order duringits progress toward fulfillment. The order tracking subsystem 306 maytrack, record, and/or update order history, order status, etc. The ordertracking subsystem 306 may store data locally (for example, in a memory)or as a portion of the order data 118 stored in the storage device 110.

In some embodiments, the system 100 may include one or more computingdevices 108, as is generally illustrated in FIG. 1B. The computingdevice 108 may include any suitable computing device, such as a mobilecomputing device, a desktop computing device, a laptop computing device,a server computing device, other suitable computing device, or acombination thereof. The computing device 108 may be used by a useraccessing the pharmacy associated with the system 100, as described.Additionally, or alternatively, the computing device 108 may beconfigured to identify an optimum or substantially optimum combinationof data objects, as described.

The computing device 108 may include a processor 130 configured tocontrol the overall operation of computing device 108. The processor 130may include any suitable processor, such as those described herein. Thecomputing device 108 may also include a user input device 132 that isconfigured to receive input from a user of the computing device 108 andto communicate signals representing the input received from the user tothe processor 130. For example, the user input device 132 may include abutton, keypad, dial, touch screen, audio input interface, visual/imagecapture input interface, input in the form of sensor data, etc.

The computing device 108 may include a display 136 that may becontrolled by the processor 130 to display information to the user. Adata bus 138 may be configured to facilitate data transfer between, atleast, a storage device 140 and the processor 130. The computing device108 may also include a network interface 142 configured to couple orconnect the computing device 108 to various other computing devices ornetwork devices via a network connection, such as a wired or wirelessconnection, such as the network 104. In some embodiments, the networkinterface 142 includes a wireless transceiver.

The storage device 140 may include a single disk or a plurality of disks(e.g., hard drives), one or more solid-state drives, one or more hybridhard drives, and the like. The storage device 140 may include a storagemanagement module that manages one or more partitions within the storagedevice 140. In some embodiments, storage device 140 may include flashmemory, semiconductor (solid state) memory or the like. The computingdevice 108 may also include a memory 144. The memory 144 may includeRandom Access Memory (RAM), a Read-Only Memory (ROM), or a combinationthereof. The memory 144 may store programs, utilities, or processes tobe executed in by the processor 130. The memory 144 may provide volatiledata storage, and stores instructions related to the operation of thecomputing device 108.

In some embodiments, the processor 130 may be configured to executeinstructions stored on the memory 144 to, at least, perform the systemsand methods described herein. In some embodiments, the computing device108 may be configured to receive a first data object, as described. Itshould be understood that the computing device 108 may receive anysuitable number of data objects. The first data object may representinformation corresponding to requests for information associated with aproject questionnaire for a respective project. The first data objectmay indicate a first value associated with a first value type. The firstvalue type may correspond to one of the request for information types,as described. The first value may correspond to information for arespective data input field on the project questionnaire. For example,the first value may correspond to information the user provided at theuser interface in response to a request for information associated withthe first value type.

In some embodiments, the first value may include a first weight value.The first data object may further indicate a second value associatedwith the first value type. The second value may correspond toinformation for another respective data input field on the projectquestionnaire. For example, the second value may correspond toinformation the user provided at the user interface in response toanother request for information associated with the first value type(e.g., the project questionnaire may include multiple requests forinformation corresponding to a respective request for information type).The second value may include a second weight value.

In some embodiments, the first data object may further indicate a thirdvalue and a fourth value associated with a second value type. The thirdvalue may include a third weight value and the fourth value may includea fourth weight value. The first data object may further indicate afifth value corresponding to a third value type. The third value typemay correspond to an effort request for information type (e.g.,indicating an amount of effort required for performing various aspectsof the respective project).

In some embodiments, the computing device 108 may determine a first sumof a product of the first value and the first weight value plus aproduct of the second value and the second weight value. The computingdevice 108 may generate a first score based on a result of the first sumdivided by a sum of the first weight value and the second weight value.

In some embodiments, the computing device 108 may determine a second sumof a product of the third value and the third weight value plus aproduct of the fourth value and the fourth weight value. The computingdevice 108 may generate a second score based on a result of the secondsum divided by a sum of the third weight value and the fourth weightvalue.

In some embodiments, the first data object may further indicate a sixthvalue associated with a fourth value type and a seventh value associatedwith the fourth value type. The sixth value may include a fifth weightvalue and the seventh value may include a sixth weight value. Thecomputing device 108 may determine a third sum of a product of the sixthvalue and the fifth weight value plus a product of the seventh value andthe sixth weight value. The computing device 108 may generate a thirdscore based on a result of the third sum divided by a sum of the fifthweight value and the sixth weight value.

In some embodiments, computing device 108 may determine a first dataobject score (e.g., a common value score for the project correspondingto the first data object) for the first data object based on the firstscore, the second score, the third score, and the fifth value. Forexample, the computing device 108 may determine a first sum of the firstscore, the second score, and the third score. The computing device 108may generate the first data object score by determining the result ofthe first sum divided by the fifth value.

In some embodiments, the computing device 108 may generate a reportincluding, at least, the first data object score and at least one otherdata object score. The first data object score and the at least oneother data object may be organized on the report according to adynamically generated order. In some embodiments, the computing device108 may output, to a display, such as the display 136 or other suitabledisplay, the report.

In some embodiments, the computing device 108 may receive a plurality ofdata objects. Each data object may include a corresponding score valueand a corresponding weight value. The computing device 108 maydetermine, using natural language processing of the plurality of dataobjects, at least one resource domain. The at least one resource domainhaving a weight value and corresponding to resources utilized for aproject associated with a corresponding data object of the plurality ofdata objects.

The computing device 108 may identify all possible data objectcombinations for at least some (e.g., according to the depth value) ofthe plurality of data objects for the at least one resource domain. Eachdata object combination may be represented by a binary data stringindicating selected data objects for a respective data objectcombination. The computing device 108 may determine a total score valuefor each data object combination of the identified possible data objectcombinations by calculating a sum of the corresponding score values foreach data object identified in a respective data object combination ofthe identified possible data object combinations.

The computing device 108 may determine a total weight value for eachdata object combination of the identified possible data objectcombinations by calculating a sum of the corresponding weight values foreach data object identified in a respective data object combination ofthe identified possible data object combinations. The computing device108 may identify data object combinations of the identified possibledata object combinations having a total weight value less than or equalto the weight value of the at least one resource domain.

The computing device 108 may identify data object combinations, of theidentified data object combinations of the possible data objectcombinations having a total weight value less than or equal to theweight value of the at least one resource domain, having a total scorevalue greater than a first total score value threshold.

The computing device 108 may generate a first set of data objectcombinations using identified data object combinations, of theidentified data object combinations of the possible data objectcombinations having a total weight value less than or equal to theweight value of the at least one resource domain, having a total scorevalue greater than the first total score value threshold.

In some embodiments, the computing device 108 may set a mutationvariable to a predetermine value. The computing device 108 may apply themutation variable to the first set of data object combinations. Thecomputing device 108 may select at least two data object combinations ofthe first set of data object combinations after application of themutation variable to the first set of data object combinations.

In some embodiments, the computing device 108 may identify data objectsthat appear in each of the at least two data object combinations of thefirst set of data object combinations. The computing device 108 maygenerate, using an artificial intelligence engine 146 configured to useat least one machine learning model 148 configured to identify dataobject combinations, a second set of data object combinations using thedata objects that appear in each of the at least two data objectcombinations of the first set of data object combinations and eachpossible combination of data objects that do not appear in at least onedata object combination of the at least two data object combinations ofthe first set of data object combinations. The artificial intelligenceengine 146 may include any suitable artificial intelligence engine andmay be disposed on computing device 108 or remotely located from thecomputing device 108, such as in a cloud computing device or othersuitable remotely located computing device.

The artificial intelligence engine 146 may use one or more machinelearning models 148 to perform at least one of the embodiments disclosedherein. The computing device 108 may include a training engine capableof generating the one or more machine learning models 148. The machinelearning models 148 may be trained to identify natural language (e.g.,human language) provided by the user in response to requests forinformation on the project questionnaire, to identify domains associatedwith data objects, and/or to iteratively identify data objectcombinations, as described.

For example, the computing device 108 may be configured to receive atleast one data object, as described. The computing device 108 mayidentify, for a first aspect of the at least one data object thatincludes free form textual information, text strings of the free formtextual information having a first text string type. The first textstring type may include a text string that indicates the text string isa stop word, as described.

The computing device 108 may, in response to identifying at least onetext string of the free form textual information having the first textstring type, generate updated free form textual information by removingthe at least one text string having the first text string type. Thecomputing device 108 may generate one or more feature vectors based onthe updated free form textual information using at least one of aunigram, a bigram, and a trigram. In some embodiments, the one or morefeature vectors include a score, such as a Tf-IDF score or othersuitable score.

In some embodiments, the computing device 108 may use the artificialintelligence engine 146 using the machine learning model 148, which maybe configured to provide, using the one or more feature vectors, anoutput that includes at least one prediction indicating at least oneresource domain and a weight value. The weight value may indicate aprobability that the at least one resource domain corresponds to thefree form textual information. In some embodiments, the machine learningmodel 148 may include a multi-layer perceptron model, such as afully-connected multi-layer perceptron model or other suitable model.The machine learning model 148 may include any suitable number oflayers, such as three layers, four layers, five layers, or any othersuitable number of layers. For example, the machine learning model 148may include an input layer (e.g., such as the first layer 802), a hiddenlayer (e.g., such as the second layer 804), and an output layer (e.g.,such as the third layer 806). Nodes associated with the hidden layer andthe output layer may use a non-linear activation function, such as thosedescribed herein.

In some embodiments, the machine learning model 148 may be initiallytrained using at least one supervised learning technique, such asbackpropagation and/or other suitable technique. In some embodiments,the machine learning model 148 may be iteratively trained using at leastthe output of the machine learning model 148.

The computing device 108 may provide, at the display 136, the output. Auser may review the output and determine which resource domains toselect for the data object. Additionally, or alternatively, the outputmay indicate the most likely resource domains to be implicated by thedata object. In some embodiments, the output of the machine learningmodel 148 may be provided as input for determining the data objectcombination and/or the data object scores, as described herein.

The machine learning model 148 may be generated by the training engineand may be implemented in computer instructions executable by one ormore processing devices of the computing device 108. To generate the oneor more machine learning models, including the machine learning model148, the training engine may train the one or more machine learningmodels using feedback provided by the user (e.g., as described) orgenerated by the computing device 108.

In some embodiments, the computing device 108 may provide, to a display,such as the display 136 or other suitable display, output indicatingdata object combinations of the second set of data object combinationshaving a total score value above a second total score value threshold.

In some embodiments, the computing device 108 and/or the system 100 mayperform the methods described herein. However, the methods describedherein as performed by the computing device 108 and/or the system 100are not meant to be limiting, and any type of software executed on acomputing device or a combination of various computing devices canperform the methods described herein without departing from the scope ofthis disclosure.

FIG. 5 is a flow diagram generally illustrating a dynamic data objectscoring method 500 according to the principles of the presentdisclosure. At 502, the method 500 receives a first data objectindicating. For example, the computing device 108 may receive the firstdata object. The first data object may indicate a first value associatedwith a first value type. The first value may include a first weightvalue. The first data object may further indicate a second valueassociated with the first value type. The second value may include asecond weight value. The first data object may further indicate a thirdvalue associated with a second value type. The third value may include athird weight value. The first data object may further indicate a fourthvalue associated with the second value type. The fourth value mayinclude a fourth weight value. The first data object may furtherindicate a fifth value corresponding to a third value type.

At 504, the method 500 may determine a first sum of a product of thefirst value and the first weight value plus a product of the secondvalue and the second weight value. For example, the computing device 108may determine the first sum of the product of the first value and thefirst weight value plus the product of the second value and the secondweight value.

At 506, the method 500 generates a first score based on a result of thefirst sum divided by a sum of the first weight value and the secondweight value. For example, the computing device 108 may generate thefirst score based on the result of the first sum divided by the sum ofthe first weight value and the second weight value.

At 508, the method 500 determines a second sum of a product of the thirdvalue and the third weight value plus a product of the fourth value andthe fourth weight value. For example, the computing device 108 maydetermine the second sum of the product of the third value and the thirdweight value plus the product of the fourth value and the fourth weightvalue.

At 510, the method 500 generates a second score based on a result of thesecond sum divided by a sum of the third weight value and the fourthweight value. For example, the computing device 108 may generate thesecond score based on the result of the second sum divided by the sum ofthe third weight value and the fourth weight value.

At 512, the method 500 determines a first data object score for thefirst data object based on, at least, the first score, the second score,and the fifth value. For example, the computing device 108 may determinethe first data object score for the first data object based on, atleast, the first score, the second score, and the fifth value.

At 514, the method 500 generates a report including, at least, the firstdata object score and at least one other data object score, the firstdata object score and the at least one other data object score beingorganized on the report according to a dynamically generated order. Forexample, the computing device 108 may generate the report including, atleast, the first data object score and at least one other data objectscore, the first data object score and the at least one other dataobject score being organized on the report according to a dynamicallygenerated order.

FIGS. 6A and 6B is a flow diagram generally illustrating a data objectevolutionary optimization method 600 according to the principles of thepresent disclosure. At 602, the method 600 receives a plurality of dataobjects, each data object having a corresponding score value and acorresponding weight value. For example, the computing device 108 mayreceive the plurality of data objects.

At 604, the method 600 determines, using natural language processing ofthe plurality of data objects, at least one resource domain, the atleast one resource domain having a weight value. For example, thecomputing device 108 may determine, using natural language processing ofthe plurality of data objects, the at least one resource domain.

At 606, the method 600 identifies all possible data object combinationsfor at least some of the plurality of data objects for the at least oneresource domain. For example, the computing device 108 may identify allpossible data object combinations for at least some of the plurality ofdata objects for the at least one resource domain. Each data objectcombination may be represented by a binary data string indicatingselected data objects for a respective data object combination.

At 608, the method 600 determines a total score value for each dataobject combination. For example, the computing device 108 may determinethe total score value for each data object combination of the identifiedpossible data object combinations by calculating a sum of thecorresponding score values for each data object identified in arespective data object combination of the identified possible dataobject combinations.

At 610, the method 600 may determine a total weight value for each dataobject combination. For example, the computing device 108 may determinethe total weight value for each data object combination of theidentified possible data object combinations by calculating a sum of thecorresponding weight values for each data object identified in arespective data object combination of the identified possible dataobject combinations.

At 612, the method 600 identifies data object combinations having atotal weight value less than or equal to the weight value of the atleast one resource domain and having a total score value greater than afirst total score value threshold. For example, the computing device 108may identify the data object combinations of the identified possibledata object combinations having a total weight value less than or equalto the weight value of the at least one resource domain. The computingdevice 108 may identify data object combinations, of the identified dataobject combinations of the possible data object combinations having atotal weight value less than or equal to the weight value of the atleast one resource domain, having a total score value greater than afirst total score value threshold.

At 616, the method 600 generates a first set of data object combinationsusing identified data object combinations, of the identified data objectcombinations of the possible data object combinations having a totalweight value less than or equal to the weight value of the at least oneresource domain, having a total score value greater than the first totalscore value threshold. For example, the computing device 108 maygenerate the first set of data object combinations using identified dataobject combinations, of the identified data object combinations of thepossible data object combinations having a total weight value less thanor equal to the weight value of the at least one resource domain, havinga total score value greater than the first total score value threshold

At 618, the method 600 sets a mutation variable to a predeterminedvalue. For example, the computing device 108 may set the mutationvariable to the predetermine value.

At 620, the method 600 applies the mutation variable to the first set ofdata object combinations. For example, the computing device 108 mayapply the mutation variable to the first set of data objectcombinations.

At 622, the method 600 selects at least two data object combinations ofthe first set of data object combinations after application of themutation variable to the first set of data object combinations. Forexample, the computing device 108 may select the at least two dataobject combinations of the first set of data object combinations afterapplication of the mutation variable to the first set of data objectcombinations.

At 624, the method 600 identifies data objects that appear in each ofthe at least two data object combinations of the first set of dataobject combinations. For example, the computing device 108 may identifythe data objects that appear in each of the at least two data objectcombinations of the first set of data object combinations.

At 626, the method 600 generates, using an artificial intelligenceengine configured to use at least one machine learning model configuredto identify data object combinations, a second set of data objectcombinations using the data objects that appear in each of the at leasttwo data object combinations of the first set of data objectcombinations and each possible combination of data objects that do notappear in at least one data object combination of the at least two dataobject combinations of the first set of data object combinations. Forexample, the computing device 108 may generate, using the artificialintelligence engine (e.g., configured to use the at least one machinelearning model configured to identify data object combinations), thesecond set of data object combinations using the data objects thatappear in each of the at least two data object combinations of the firstset of data object combinations and each possible combination of dataobjects that do not appear in at least one data object combination ofthe at least two data object combinations of the first set of dataobject combinations.

At 628, the method 600 provides, to a display, output indicating dataobject combinations of the second set of data object combinations havinga total score value above a second total score value threshold. Forexample, the computing device 108 may provide, to a display, such as thedisplay 136 or other suitable display, output indicating the data objectcombinations of the second set of data object combinations having atotal score value above a second total score value threshold.

FIG. 7 is a flow diagram generally illustrating a natural languageprocessing method according to the principles of the present disclosure.At 702, the method 700 receives at least one data object. For example,the computing device 108 may receive the at least one data object.

At 704, the method 700 identifies, for a first aspect of the at leastone data object that includes free form textual information, textstrings of the free form textual information having a first text stringtype. For example, the computing device 108 may identify, for the firstaspect of the at least one data object that includes free form textualinformation, text strings of the free form textual information havingthe first text string type.

At 706, the method 700, in response to identifying at least one textstring of the free form textual information having the first text stringtype, generates updated free form textual information by removing the atleast one text string having the first text string type. For example,the computing device 108 may generate, in response to identifying atleast one text string of the free form textual information having thefirst text string type, updated free form textual information byremoving the at least one text string having the first text string type.

At 708, the method 700 generates one or more feature vectors based onthe updated free form textual information using at least one of aunigram, a bigram, and a trigram. For example, the computing device 700may generate the one or more feature vectors using the updated free formtextual information. The feature vectors may include at least one of aunigram, a bigram, and a trigram.

At 710, the method 700 uses an artificial intelligence engine that usesat least one machine learning model configured to provide, using the oneor more feature vectors, an output that includes at least one predictionindicating at least one resource domain and a weight value indicating aprobability that the at least one resource domain corresponds to thefree form textual information. For example, the computing device 108 mayuse the artificial intelligence engine 146 that uses the machinelearning model 148 to provide, using the one or more feature vectors,the output. The output may include at least one prediction indicating atleast one resource domain and a corresponding weight value. The weightvalue may indicate a probability that the at least one resource domaincorresponds to the free form textual information of the at least onedata object.

At 712, the method 700 provides, at a display, the output. For example,the computing device 108 may provide, at the display 136, the output.Additionally, or alternatively, the computing device 108 may use theoutput of the machine learning model 148 as input for generating thedata objection combinations and/or the data object scores, as describedherein.

In some embodiments, a system for dynamically scoring aspects of a dataobject includes a processor and a memory. The memory includesinstructions that, when executed by the processor, cause the processorto: receive a first data object indicating: a first value associatedwith a first value type, the first value corresponding to a first weightvalue; a second value associated with the first value type, the secondvalue corresponding to a second weight value; a third value associatedwith a second value type, the third value corresponding to a thirdweight value; a fourth value associated with the second value type, thefourth value corresponding to a fourth weight value; and a fifth valuecorresponding to a third value type; determine a first sum of a productof the first value and the first weight value plus a product of thesecond value and the second weight value; generate a first score basedon a result of the first sum divided by a sum of the first weight valueand the second weight value; determine a second sum of a product of thethird value and the third weight value plus a product of the fourthvalue and the fourth weight value; generate a second score based on aresult of the second sum divided by a sum of the third weight value andthe fourth weight value; and determine a first data object score for thefirst data object based on, at least, the first score, the second score,and the fifth value.

In some embodiments, the first data object further indicates a sixthvalue associated with a fourth value type and a seventh value associatedwith the fourth value type, the sixth value corresponding to a fifthweight value and the seventh value corresponding to a sixth weightvalue. In some embodiments, the instructions further cause the processorto: determine a third sum of a product of the sixth value and the fifthweight value plus a product of the seventh value and the sixth weightvalue; and generate a third score based on a result of the third sumdivided by a sum of the fifth weight value and the sixth weight value.In some embodiments, the instructions further cause the processor todetermine the first data object score further based on the third score.In some embodiments, the first score corresponds to one of a businessvaluation and a time criticality. In some embodiments, the second scorecorresponds to the other of the business valuation and the timecriticality. In some embodiments, the fifth value corresponds to anamount of effort associated with execution of a project associated withthe first data object. In some embodiments, the instructions furthercause the processor to generate a report comprising, at least, the firstdata object score and at least one other data object score, wherein thefirst data object score and the at least one other data object areorganized on the report according to a dynamically generated order. Insome embodiments, the instructions further cause the processor tooutput, to a display, the report.

In some embodiments, a method for dynamically scoring aspects of a dataobject includes receiving a first data object indicating: a first valueassociated with a first value type, the first value corresponding to afirst weight value; a second value associated with the first value type,the second value corresponding to a second weight value; a third valueassociated with a second value type, the third value corresponding to athird weight value; a fourth value associated with the second valuetype, the fourth value corresponding to a fourth weight value; and afifth value corresponding to a third value type. The method alsoincludes determining a first sum of a product of the first value and thefirst weight value plus a product of the second value and the secondweight value and generating a first score based on a result of the firstsum divided by a sum of the first weight value and the second weightvalue. The method also includes determining a second sum of a product ofthe third value and the third weight value plus a product of the fourthvalue and the fourth weight value and generating a second score based ona result of the second sum divided by a sum of the third weight valueand the fourth weight value. The method also includes determining afirst data object score for the first data object based on, at least,the first score, the second score, and the fifth value.

In some embodiments, the first data object further indicates a sixthvalue associated with a fourth value type and a seventh value associatedwith the fourth value type, the sixth value corresponding to a fifthweight value and the seventh value corresponding to a sixth weightvalue. In some embodiments, the method also includes: determining athird sum of a product of the sixth value and the fifth weight valueplus a product of the seventh value and the sixth weight value; andgenerating a third score based on a result of the third sum divided by asum of the fifth weight value and the sixth weight value. In someembodiments, the method also includes determining the first data objectscore further based on the third score. In some embodiments, the firstscore corresponds to one of a business valuation and a time criticality.In some embodiments, the second score corresponds to the other of thebusiness valuation and the time criticality. In some embodiments, thefifth value corresponds to an amount of effort associated with executionof a project associated with the first data object. In some embodiments,the method also includes generating a report comprising, at least, thefirst data object score and at least one other data object score,wherein the first data object score and the at least one other dataobject are organized on the report according to a dynamically generatedorder. In some embodiments, the method also includes outputting, to adisplay, the report.

In some embodiments an apparatus for dynamically scoring aspects of adata object includes a processor and a memory. The memory includesinstructions that, when executed by the processor, cause the processorto: receive a first data object indicating: a first value associatedwith a first value type, the first value corresponding to a first weightvalue; a second value associated with the first value type, the secondvalue corresponding to a second weight value; a third value associatedwith a second value type, the third value corresponding to a thirdweight value; a fourth value associated with the second value type, thefourth value corresponding to a fourth weight value; and a fifth valuecorresponding to a third value type, the fifth value corresponding to anamount of effort associated with execution of a project associated withthe first data object; determine a first sum of a product of the firstvalue and the first weight value plus a product of the second value andthe second weight value; generate a first score based on a result of thefirst sum divided by a sum of the first weight value and the secondweight value; determine a second sum of a product of the third value andthe third weight value plus a product of the fourth value and the fourthweight value; generate a second score based on a result of the secondsum divided by a sum of the third weight value and the fourth weightvalue; determine a first data object score for the first data objectbased on, at least, the first score, the second score, and the fifthvalue; and generate a report comprising, at least, the first data objectscore and at least one other data object score, wherein the first dataobject score and the at least one other data object are organized on thereport according to a dynamically generated order.

In some embodiments, the instructions further cause the processor tooutput, to a display, the report.

In some embodiments, an evolutionary optimization system includes aprocessor and a memory. The memory includes instructions that, whenexecuted by the processor, cause the processor to: receive a pluralityof data objects, each data object having a corresponding score value anda corresponding weight value; identify all possible data objectcombinations for at least some of the plurality of data objects, eachdata object combination being represented by a binary data string;determine a total score value for each data object combination of theidentified possible data object combinations by calculating a sum of thecorresponding score values for each data object identified in arespective data object combination of the identified possible dataobject combinations; generate a first set of data object combinationsusing the total score value for each data object combination of theidentified possible data object combinations and at least a total weightvalue for each data object combination of the identified possible dataobject combinations; set a mutation variable to a predetermine value;apply the mutation variable to the first set of data objectcombinations; select at least two data object combinations of the firstset of data object combinations after application of the mutationvariable to the first set of data object combinations; identify dataobjects that appear in each of the at least two data object combinationsof the first set of data object combinations; and generate a second setof data object combinations using the data objects that appear in eachof the at least two data object combinations of the first set of dataobject combinations and each possible combination of data objects thatdo not appear in at least one data object combination of the at leasttwo data object combinations of the first set of data objectcombinations.

In some embodiments, the at least some of the plurality of data objectsused to identify all possible data object combinations corresponds to apredetermined data object list depth. In some embodiments, theinstructions further cause the processor to determine a total scorevalue for each data object combination of the second set of data objectcombinations by calculating a sum of the corresponding score values foreach data object identified in a respective data object combination ofthe second set of data object combinations. In some embodiments, theinstructions further cause the processor to identify a third set of dataobject combinations that includes data object combinations of the secondset of data object combinations having a total score value above athreshold total score value and a at least a total weight value for eachdata object combination of the second set of data object combinations.In some embodiments, the threshold total score value includes a highesttotal score value of the data object combinations of the first set ofdata object combinations. In some embodiments, a score value for arespective data object corresponds to a positive benefit derived fromthe respective data object. In some embodiments, a weight value for arespective data object corresponds to a cost derived from the respectivedata object. In some embodiments, the instructions further cause theprocessor to generate the second set of data object combinations usingan artificial intelligence engine configured to use at least one machinelearning model trained to identify data object combinations. In someembodiments, the at least one machine learning model is initiallytrained using a plurality of previously identified data objectcombinations. In some embodiments, the at least one machine learningmodel is iteratively trained using at least output of the at least onemachine learning model. In some embodiments, each data objectcombination corresponds to a resource domain and wherein theinstructions further cause the processor to identify resource domainsfor each data object combination using natural language processing.

In some embodiments, a method for providing evolutionary optimizationincludes receiving a plurality of data objects, each data object havinga corresponding score value and a corresponding weight value andidentifying all possible data object combinations for at least some ofthe plurality of data objects, each data object combination beingrepresented by a binary data string. The method also includesdetermining a total score value for each data object combination of theidentified possible data object combinations by calculating a sum of thecorresponding score values for each data object identified in arespective data object combination of the identified possible dataobject combinations and generating a first set of data objectcombinations using the total score value for each data objectcombination of the identified possible data object combinations and atleast a total weight value for each data object combination of theidentified possible data object combinations. The method also includessetting a mutation variable to a predetermine value and applying themutation variable to the first set of data object combinations. Themethod also includes selecting at least two data object combinations ofthe first set of data object combinations after application of themutation variable to the first set of data object combinations andidentifying data objects that appear in each of the at least two dataobject combinations of the first set of data object combinations. Themethod also includes generating a second set of data object combinationsusing the data objects that appear in each of the at least two dataobject combinations of the first set of data object combinations andeach possible combination of data objects that do not appear in at leastone data object combination of the at least two data object combinationsof the first set of data object combinations.

In some embodiments, the at least some of the plurality of data objectsused to identify all possible data object combinations corresponds to apredetermined data object list depth. In some embodiments, the methodalso includes determining a total score value for each data objectcombination of the second set of data object combinations by calculatinga sum of the corresponding score values for each data object identifiedin a respective data object combination of the second set of data objectcombinations. In some embodiments, the method also includes identifyinga third set of data object combinations that includes data objectcombinations of the second set of data object combinations having atotal score value above a threshold total score value and a at least atotal weight value for each data object combination of the second set ofdata object combinations. In some embodiments, the threshold total scorevalue includes a highest total score value of the data objectcombinations of the first set of data object combinations. In someembodiments, a score value for a respective data object corresponds to apositive benefit derived from the respective data object. In someembodiments, a weight value for a respective data object corresponds toa cost derived from the respective data object. In some embodiments, themethod also includes generating the second set of data objectcombinations using an artificial intelligence engine configured to useat least one machine learning model trained to identify data objectcombinations.

In some embodiments, an evolutionary optimization apparatus includes aprocessor and a memory that includes instructions that, when executed bythe processor, cause the processor to: receive a plurality of dataobjects, each data object having a corresponding score value and acorresponding weight value; determine, using natural language processingof the plurality of data objects, at least one resource domain, the atleast one resource domain having a weight value; identify all possibledata object combinations for at least some of the plurality of dataobjects for the at least one resource domain, each data objectcombination being represented by a binary data string indicatingselected data objects for a respective data object combination;determine a total score value for each data object combination of theidentified possible data object combinations by calculating a sum of thecorresponding score values for each data object identified in arespective data object combination of the identified possible dataobject combinations; determine a total weight value for each data objectcombination of the identified possible data object combinations bycalculating a sum of the corresponding weight values for each dataobject identified in a respective data object combination of theidentified possible data object combinations; identify data objectcombinations of the identified possible data object combinations havinga total weight value less than or equal to the weight value of the atleast one resource domain; identify data object combinations, of theidentified data object combinations of the possible data objectcombinations having a total weight value less than or equal to theweight value of the at least one resource domain, having a total scorevalue greater than a first total score value threshold; generate a firstset of data object combinations using identified data objectcombinations, of the identified data object combinations of the possibledata object combinations having a total weight value less than or equalto the weight value of the at least one resource domain, having a totalscore value greater than the first total score value threshold; set amutation variable to a predetermine value; apply the mutation variableto the first set of data object combinations; select at least two dataobject combinations of the first set of data object combinations afterapplication of the mutation variable to the first set of data objectcombinations; identify data objects that appear in each of the at leasttwo data object combinations of the first set of data objectcombinations; generate, using an artificial intelligence engineconfigured to use at least one machine learning model configured toidentify data object combinations, a second set of data objectcombinations using the data objects that appear in each of the at leasttwo data object combinations of the first set of data objectcombinations and each possible combination of data objects that do notappear in at least one data object combination of the at least two dataobject combinations of the first set of data object combinations; andprovide, to a display, output indicating data object combinations of thesecond set of data object combinations having a total score value abovea second total score value threshold.

In some embodiments, a system for processing natural language includes aprocessor and a memory. The memory includes instructions that, whenexecuted by the processor, cause the processor to: receive at least onedata object; identify, for a first aspect of the at least one dataobject that includes free form textual information, text strings of thefree form textual information having a first text string type; inresponse to identifying at least one text string of the free formtextual information having the first text string type, generate updatedfree form textual information by removing the at least one text stringhaving the first text string type; generate one or more feature vectorsbased on the updated free form textual information using at least one ofa unigram, a bigram, and a trigram; use an artificial intelligenceengine that uses at least one machine learning model configured toprovide, using the one or more feature vectors, an output that includesat least one prediction indicating at least one resource domain and aweight value indicating a probability that the at least one resourcedomain corresponds to the free form textual information; and provide, ata display, the output.

In some embodiments, the at least one machine learning model includes amulti-layer perceptron model. In some embodiments, the at least onemachine learning model includes a fully-connected multi-layer perceptronmodel. In some embodiments, the at least one machine learning modelincludes at least an input layer, a hidden layer, and an output layer.In some embodiments, nodes associated with the hidden layer and theoutput layer use a non-linear activation function. In some embodiments,the machine learning model is initially trained using a supervisedlearning technique. In some embodiments, the supervised learningtechnique incudes backpropagation. In some embodiments, the machinelearning model is iteratively trained using at least the output of theat least one machine learning model. In some embodiments, the one ormore feature vectors include a term frequency-inverse document frequencyscore.

In some embodiments, a method for processing natural language includesreceiving at least one data object and identifying, for a first aspectof the at least one data object that includes free form textualinformation, text strings of the free form textual information having afirst text string type. The method also includes, in response toidentifying at least one text string of the free form textualinformation having the first text string type, generating updated freeform textual information by removing the at least one text string havingthe first text string type. The method also includes generating one ormore feature vectors based on the updated free form textual informationusing at least one of a unigram, a bigram, and a trigram. The methodalso includes using an artificial intelligence engine that uses at leastone machine learning model configured to provide, using the one or morefeature vectors, an output that includes at least one predictionindicating at least one resource domain and a weight value indicating aprobability that the at least one resource domain corresponds to thefree form textual information. The method also includes providing, at adisplay, the output.

In some embodiments, the at least one machine learning model includes amulti-layer perceptron model. In some embodiments, the at least onemachine learning model includes a fully-connected multi-layer perceptronmodel. In some embodiments, the at least one machine learning modelincludes at least an input layer, a hidden layer, and an output layer.In some embodiments, nodes associated with the hidden layer and theoutput layer use a non-linear activation function. In some embodiments,the machine learning model is initially trained using a supervisedlearning technique. In some embodiments, the supervised learningtechnique incudes backpropagation. In some embodiments, the machinelearning model is iteratively trained using at least the output of theat least one machine learning model. In some embodiments, the one ormore feature vectors include a term frequency-inverse document frequencyscore.

In some embodiments, an apparatus for processing natural languageincludes a processor and a memory. The memory includes instructionsthat, when executed by the processor, cause the processor to: receive atleast one data object having an aspect that includes free form textualinformation corresponding to a project description; identify textstrings of the free form textual information having a first text stringtype; in response to identifying at least one text string of the freeform textual information having the first text string type, generateupdated free form textual information by removing the at least one textstring having the first text string type; generate a plurality offeature vectors using the updated free form textual information, whereineach feature vector of the plurality of feature vectors includes atleast one of a unigram, a bigram, and a trigram; use an artificialintelligence engine that uses at least one machine learning modelconfigured to provide, using the plurality of feature vectors, an outputthat includes at least one prediction indicating at least one resourcedomain and a weight value indicating a probability that the at least oneresource domain corresponds to the at least one data object, wherein themachine learning model is initially trained using a supervised learningtechnique and iteratively trained using at least the output of the atleast one machine learning model; and provide, at a display, the output.

In some embodiments, the plurality of feature vectors each include aterm frequency-inverse document frequency score.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present invention. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the above disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Asused herein, the phrase at least one of A, B, and C should be construedto mean a logical (A OR B OR C), using a non-exclusive logical OR, andshould not be construed to mean “at least one of A, at least one of B,and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A. The term subset doesnot necessarily require a proper subset. In other words, a first subsetof a first set may be coextensive with (equal to) the first set.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuit(s) may implement wired or wireless interfaces thatconnect to a local area network (LAN) or a wireless personal areanetwork (WPAN). Examples of a LAN are Institute of Electrical andElectronics Engineers (IEEE) Standard 802.11-2016 (also known as theWIFI wireless networking standard) and IEEE Standard 802.3-2015 (alsoknown as the ETHERNET wired networking standard). Examples of a WPAN arethe BLUETOOTH wireless networking standard from the Bluetooth SpecialInterest Group and IEEE Standard 802.15.4.

The module may communicate with other modules using the interfacecircuit(s). Although the module may be depicted in the presentdisclosure as logically communicating directly with other modules, invarious implementations the module may actually communicate via acommunications system. The communications system includes physicaland/or virtual networking equipment such as hubs, switches, routers, andgateways. In some implementations, the communications system connects toor traverses a wide area network (WAN) such as the Internet. Forexample, the communications system may include multiple LANs connectedto each other over the Internet or point-to-point leased lines usingtechnologies including Multiprotocol Label Switching (MPLS) and virtualprivate networks (VPNs).

In various implementations, the functionality of the module may bedistributed among multiple modules that are connected via thecommunications system. For example, multiple modules may implement thesame functionality distributed by a load balancing system. In a furtherexample, the functionality of the module may be split between a server(also known as remote, or cloud) module and a client (or, user) module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of a non-transitory computer-readable medium are nonvolatilememory devices (such as a flash memory device, an erasable programmableread-only memory device, or a mask read-only memory device), volatilememory devices (such as a static random access memory device or adynamic random access memory device), magnetic storage media (such as ananalog or digital magnetic tape or a hard disk drive), and opticalstorage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium. Thecomputer programs may also include or rely on stored data. The computerprograms may encompass a basic input/output system (BIOS) that interactswith hardware of the special purpose computer, device drivers thatinteract with particular devices of the special purpose computer, one ormore operating systems, user applications, background services,background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation), (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C#,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

Implementations of the systems, algorithms, methods, instructions, etc.,described herein may be realized in hardware, software, or anycombination thereof. The hardware may include, for example, computers,intellectual property (IP) cores, application-specific integratedcircuits (ASICs), programmable logic arrays, optical processors,programmable logic controllers, microcode, microcontrollers, servers,microprocessors, digital signal processors, or any other suitablecircuit. In the claims, the term “processor” should be understood asencompassing any of the foregoing hardware, either singly or incombination. The terms “signal” and “data” are used interchangeably.

What is claimed is:
 1. A system for processing natural language, thesystem comprising: a processor; and a memory including instructionsthat, when executed by the processor, cause the processor to: receive atleast one data object; identify, for a first aspect of the at least onedata object that includes free form textual information, text strings ofthe free form textual information having a first text string type; inresponse to identifying at least one text string of the free formtextual information having the first text string type, generate updatedfree form textual information by removing the at least one text stringhaving the first text string type; generate one or more feature vectorsbased on the updated free form textual information using at least one ofa unigram, a bigram, and a trigram; use an artificial intelligenceengine that uses at least one machine learning model configured toprovide, using the one or more feature vectors, an output that includesat least one prediction indicating at least one resource domain and aweight value indicating a probability that the at least one resourcedomain corresponds to the free form textual information; and provide, ata display, the output.
 2. The system of claim 1, wherein the at leastone machine learning model includes a multi-layer perceptron model. 3.The system of claim 1, wherein the at least one machine learning modelincludes a fully-connected multi-layer perceptron model.
 4. The systemof claim 1, wherein the at least one machine learning model includes atleast an input layer, a hidden layer, and an output layer.
 5. The systemof claim 4, wherein nodes associated with the hidden layer and theoutput layer use a non-linear activation function.
 6. The system ofclaim 1, wherein the at least one machine learning model is initiallytrained using a supervised learning technique.
 7. The system of claim 6,wherein the supervised learning technique incudes backpropagation. 8.The system of claim 1, wherein the at least one machine learning modelis iteratively trained using at least the output of the at least onemachine learning model.
 9. The system of claim 1, wherein the one ormore feature vectors include a term frequency-inverse document frequencyscore.
 10. A method for processing natural language, the methodcomprising: receiving at least one data object; identifying, for a firstaspect of the at least one data object that includes free form textualinformation, text strings of the free form textual information having afirst text string type; in response to identifying at least one textstring of the free form textual information having the first text stringtype, generating updated free form textual information by removing theat least one text string having the first text string type; generatingone or more feature vectors based on the updated free form textualinformation using at least one of a unigram, a bigram, and a trigram;using an artificial intelligence engine that uses at least one machinelearning model configured to provide, using the one or more featurevectors, an output that includes at least one prediction indicating atleast one resource domain and a weight value indicating a probabilitythat the at least one resource domain corresponds to the free formtextual information; and providing, at a display, the output.
 11. Themethod of claim 10, wherein the at least one machine learning modelincludes a multi-layer perceptron model.
 12. The method of claim 10,wherein the at least one machine learning model includes afully-connected multi-layer perceptron model.
 13. The method of claim10, wherein the at least one machine learning model includes at least aninput layer, a hidden layer, and an output layer.
 14. The method ofclaim 13, wherein nodes associated with the hidden layer and the outputlayer use a non-linear activation function.
 15. The method of claim 10,wherein the at least one machine learning model is initially trainedusing a supervised learning technique.
 16. The method of claim 15,wherein the supervised learning technique incudes backpropagation. 17.The method of claim 10, wherein the at least one machine learning modelis iteratively trained using at least the output of the at least onemachine learning model.
 18. The method of claim 10, wherein the one ormore feature vectors include a term frequency-inverse document frequencyscore.
 19. An apparatus for processing natural language comprising: aprocessor; and a memory including instructions that, when executed bythe processor, cause the processor to: receive at least one data objecthaving an aspect that includes free form textual informationcorresponding to a project description; identify text strings of thefree form textual information having a first text string type; inresponse to identifying at least one text string of the free formtextual information having the first text string type, generate updatedfree form textual information by removing the at least one text stringhaving the first text string type; generate a plurality of featurevectors using the updated free form textual information, wherein eachfeature vector of the plurality of feature vectors includes at least oneof a unigram, a bigram, and a trigram; use an artificial intelligenceengine that uses at least one machine learning model configured toprovide, using the plurality of feature vectors, an output that includesat least one prediction indicating at least one resource domain and aweight value indicating a probability that the at least one resourcedomain corresponds to the at least one data object, wherein the machinelearning model is initially trained using a supervised learningtechnique and iteratively trained using at least the output of the atleast one machine learning model; and provide, at a display, the output.20. The apparatus of claim 19, wherein the plurality of feature vectorseach include a term frequency-inverse document frequency score.